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structural damage detection and localization with unknown post-damage feature distribution using sequential change-point detection method | the high structural deficient rate poses serious risks to the operation of many bridges and buildings. to prevent critical damage and structural collapse, a quick structural health diagnosis tool is needed during normal operation or immediately after extreme events. in structural health monitoring (shm), many existing works will have limited performance in the quick damage identification process because 1) the damage event needs to be identified with short delay and 2) the post-damage information is usually unavailable. to address these drawbacks, we propose a new damage detection and localization approach based on stochastic time series analysis. specifically, the damage sensitive features are extracted from vibration signals and follow different distributions before and after a damage event. hence, we use the optimal change point detection theory to find damage occurrence time. as the existing change point detectors require the post-damage feature distribution, which is unavailable in shm, we propose a maximum likelihood method to learn the distribution parameters from the time-series data. the proposed damage detection using estimated parameters also achieves the optimal performance. also, we utilize the detection results to find damage location without any further computation. validation results show highly accurate damage identification in american society of civil engineers benchmark structure and two shake table experiments. |
the role of body mass index at diagnosis on black-white disparities in colorectal cancer survival: a density regression mediation approach | the study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. in the case of colorectal cancer (crc), disparities in survival among non-hispanic whites and blacks are well documented, and mechanisms leading to these disparities need to be studied formally. it has also been established that body mass index (bmi) is a risk factor for developing crc, and recent literature shows bmi at diagnosis of crc is associated with survival. since bmi varies by racial/ethnic group, a question that arises is whether disparities in bmi is partially responsible for observed racial/ethnic disparities in crc survival. this paper presents new methodology to quantify the impact of the hypothetical intervention that matches the bmi distribution in the black population to a potentially complex distributional form observed in the white population on racial/ethnic disparities in survival. we perform a simulation that shows our proposed bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing bmi leads to large biases. when applied to motivating data from the cancer care outcomes research and surveillance (cancors) consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low income black patients, yet harmful for young and high income black populations. |
automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction | background: predictive modeling is a key component of solutions to many healthcare problems. among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. methods: this paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. we did a computer coding implementation of the method. using the electronic medical record data set from the practice fusion diabetes classification competition containing patient records from all 50 states in the united states, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. results: for the champion machine learning model of the competition, our method explained prediction results for 87.4% of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. conclusions: our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy. |
progressive sampling-based bayesian optimization for efficient and automatic machine learning model selection | purpose: machine learning is broadly used for clinical data analysis. before training a model, a machine learning algorithm must be selected. also, the values of one or more model parameters termed hyper-parameters must be set. selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. to lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. existing automatic selection methods are inefficient on large data sets. this poses a challenge for using machine learning in the clinical big data era. methods: to address the challenge, this paper presents progressive sampling-based bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. results: we report an implementation of the method. we show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. conclusions: this is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks. |
eeg classification based on image configuration in social anxiety disorder | the problem of detecting the presence of social anxiety disorder (sad) using electroencephalography (eeg) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of eeg sensor spatial configuration. two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. performance of these two models is examined for analyzing 34 eeg data channels each consisting of five frequency bands and further decomposed with a filter bank. the data are collected from 64 subjects consisting of healthy controls and patients with sad. validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. convolutional neural networks (cnn) were found to provide much better performance than svm and knns. |
measuring and characterizing generalization in deep reinforcement learning | deep reinforcement-learning methods have achieved remarkable performance on challenging control tasks. observations of the resulting behavior give the impression that the agent has constructed a generalized representation that supports insightful action decisions. we re-examine what is meant by generalization in rl, and propose several definitions based on an agent's performance in on-policy, off-policy, and unreachable states. we propose a set of practical methods for evaluating agents with these definitions of generalization. we demonstrate these techniques on a common benchmark task for deep rl, and we show that the learned networks make poor decisions for states that differ only slightly from on-policy states, even though those states are not selected adversarially. taken together, these results call into question the extent to which deep q-networks learn generalized representations, and suggest that more experimentation and analysis is necessary before claims of representation learning can be supported. |
a new multilayer optical film optimal method based on deep q-learning | multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. these algorithms rapidly promote the design and manufacture of multi-layer films. however, traditional numerical algorithms of converge to local optimum. this means that the algorithms can not give a global optimal solution to the material researchers. in recent years, due to the rapid development to far artificial intelligence, to optimize optical film structure using ai algorithm has become possible. in this paper, we will introduce a new optical film design algorithm based on the deep q learning. this model can converge the global optimum of the optical thin film structure, this will greatly improve the design efficiency of multi-layer films. |
adversarial attacks, regression, and numerical stability regularization | adversarial attacks against neural networks in a regression setting are a critical yet understudied problem. in this work, we advance the state of the art by investigating adversarial attacks against regression networks and by formulating a more effective defense against these attacks. in particular, we take the perspective that adversarial attacks are likely caused by numerical instability in learned functions. we introduce a stability inducing, regularization based defense against adversarial attacks in the regression setting. our new and easy to implement defense is shown to outperform prior approaches and to improve the numerical stability of learned functions. |
three tools for practical differential privacy | differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy. we introduce three tools to make differentially private machine learning more practical: (1) simple sanity checks which can be carried out in a centralized manner before training, (2) an adaptive clipping bound to reduce the effective number of tuneable privacy parameters, and (3) we show that large-batch training improves model performance. |
applied federated learning: improving google keyboard query suggestions | federated learning is a distributed form of machine learning where both the training data and model training are decentralized. in this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. we describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. in whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy. |
when bifidelity meets cokriging: an efficient physics-informed multifidelity method | in this work, we propose a framework that combines the approximation-theory-based multifidelity method and gaussian-process-regression-based multifidelity method to achieve data-model convergence when stochastic simulation models and sparse accurate observation data are available. specifically, the two types of multifidelity methods we use are the bifidelity and cokriging methods. the new approach uses the bifidelity method to efficiently estimate the empirical mean and covariance of the stochastic simulation outputs, then it uses these statistics to construct a gaussian process (gp) representing low-fidelity in cokriging. we also combine the bifidelity method with kriging, where the approximated empirical statistics are used to construct the gp as well. we prove that the resulting posterior mean by the new physics-informed approach preserves linear physical constraints up to an error bound. by using this method, we can obtain an accurate construction of a state of interest based on a partially correct physical model and a few accurate observations. we present numerical examples to demonstrate performance of the method. |
online learning and decision-making under generalized linear model with high-dimensional data | we propose a minimax concave penalized multi-armed bandit algorithm under generalized linear model (g-mcp-bandit) for a decision-maker facing high-dimensional data in an online learning and decision-making process. we demonstrate that the g-mcp-bandit algorithm asymptotically achieves the optimal cumulative regret in the sample size dimension t , o(log t), and further attains a tight bound in the covariate dimension d, o(log d). in addition, we develop a linear approximation method, the 2-step weighted lasso procedure, to identify the mcp estimator for the g-mcp-bandit algorithm under non-iid samples. under this procedure, the mcp estimator matches the oracle estimator with high probability and converges to the true parameters with the optimal convergence rate. finally, through experiments based on synthetic data and two real datasets (warfarin dosing dataset and tencent search advertising dataset), we show that the g-mcp-bandit algorithm outperforms other benchmark algorithms, especially when there is a high level of data sparsity or the decision set is large. |
back to square one: probabilistic trajectory forecasting without bells and whistles | we introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. applied in an auto-regressive way it provides an explicit probability distribution over continuations of a given initial trajectory segment. we discuss it in relation to (more complicated) existing work and report on experiments on two standard datasets for trajectory forecasting: mnistseq and stanford drones, achieving results on-par with or better than previous methods. |
a biconvex analysis for lasso l1 reweighting | l1 reweighting algorithms are very popular in sparse signal recovery and compressed sensing, since in the practice they have been observed to outperform classical l1 methods. nevertheless, the theoretical analysis of their convergence is a critical point, and generally is limited to the convergence of the functional to a local minimum or to subsequence convergence. in this letter, we propose a new convergence analysis of a lasso l1 reweighting method, based on the observation that the algorithm is an alternated convex search for a biconvex problem. based on that, we are able to prove the numerical convergence of the sequence of the iterates generated by the algorithm. this is not yet the convergence of the sequence, but it is close enough for practical and numerical purposes. furthermore, we propose an alternative iterative soft thresholding procedure, which is faster than the main algorithm. |
on batch orthogonalization layers | batch normalization has become ubiquitous in many state-of-the-art nets. it accelerates training and yields good performance results. however, there are various other alternatives to normalization, e.g. orthonormalization. the objective of this paper is to explore the possible alternatives to channel normalization with orthonormalization layers. the performance of the algorithms are compared together with bn with prescribed performance measures. |
open problems in engineering and quality assurance of safety critical machine learning systems | fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance concepts and techniques. at the same time, open problems and the relevant technical fields are not organized. to establish standard quality assurance frameworks, it is necessary to visualize and organize these open problems in an interdisciplinary way, so that the experts from many different technical fields may discuss these problems in depth and develop solutions. in the present study, we identify, classify, and explore the open problems in quality assurance of safety-critical machine-learning systems, and their relevant corresponding industry and technological trends, using automated-driving vehicles as an example. our results show that addressing these open problems requires incorporating knowledge from several different technological and industrial fields, including the automobile industry, statistics, software engineering, and machine learning. |
combatting adversarial attacks through denoising and dimensionality reduction: a cascaded autoencoder approach | machine learning models are vulnerable to adversarial attacks that rely on perturbing the input data. this work proposes a novel strategy using autoencoder deep neural networks to defend a machine learning model against two gradient-based attacks: the fast gradient sign attack and fast gradient attack. first we use an autoencoder to denoise the test data, which is trained with both clean and corrupted data. then, we reduce the dimension of the denoised data using the hidden layer representation of another autoencoder. we perform this experiment for multiple values of the bound of adversarial perturbations, and consider different numbers of reduced dimensions. when the test data is preprocessed using this cascaded pipeline, the tested deep neural network classifier yields a much higher accuracy, thus mitigating the effect of the adversarial perturbation. |
principal components analysis of regularly varying functions | the paper is concerned with asymptotic properties of the principal components analysis of functional data. the currently available results assume the existence of the fourth moment. we develop analogous results in a setting which does not require this assumption. instead, we assume that the observed functions are regularly varying. we derive the asymptotic distribution of the sample covariance operator and of the sample functional principal components. we obtain a number of results on the convergence of moments and almost sure convergence. we apply the new theory to establish the consistency of the regression operator in a functional linear model. |
variable selection in high-dimensional linear model with possibly asymmetric or heavy-tailed errors | we consider the problem of automatic variable selection in a linear model with asymmetric or heavy-tailed errors when the number of explanatory variables diverges with the sample size. for this high-dimensional model, the penalized least square method is not appropriate and the quantile framework makes the inference more difficult because to the non differentiability of the loss function. we propose and study an estimation method by penalizing the expectile process with an adaptive lasso penalty. two cases are considered: the number of model parameters is smaller and afterwards larger than the sample size, the two cases being distinct by the adaptive penalties considered. for each case we give the rate convergence and establish the oracle properties of the adaptive lasso expectile estimator. the proposed estimators are evaluated through monte carlo simulations and compared with the adaptive lasso quantile estimator. we applied also our estimation method to real data in genetics when the number of parameters is greater than the sample size. |
deep variational transfer: transfer learning through semi-supervised deep generative models | in real-world applications, it is often expensive and time-consuming to obtain labeled examples. in such cases, knowledge transfer from related domains, where labels are abundant, could greatly reduce the need for extensive labeling efforts. in this scenario, transfer learning comes in hand. in this paper, we propose deep variational transfer (dvt), a variational autoencoder that transfers knowledge across domains using a shared latent gaussian mixture model. thanks to the combination of a semi-supervised elbo and parameters sharing across domains, we are able to simultaneously: (i) align all supervised examples of the same class into the same latent gaussian mixture component, independently from their domain; (ii) predict the class of unsupervised examples from different domains and use them to better model the occurring shifts. we perform tests on mnist and usps digits datasets, showing dvt's ability to perform transfer learning across heterogeneous datasets. additionally, we present dvt's top classification performances on the mnist semi-supervised learning challenge. we further validate dvt on a astronomical datasets. dvt achieves states-of-the-art classification performances, transferring knowledge across real stars surveys datasets, eros, macho and hits, . in the worst performance, we double the achieved f1-score for rare classes. these experiments show dvt's ability to tackle all major challenges posed by transfer learning: different covariate distributions, different and highly imbalanced class distributions and different feature spaces. |
playing with and against hedge | hedge has been proposed as an adaptive scheme, which guides an agent's decision in resource selection and distribution problems that can be modeled as a multi-armed bandit full information game. such problems are encountered in the areas of computer and communication networks, e.g. network path selection, load distribution, network interdiction, and also in problems in the area of transportation. we study hedge under the assumption that the total loss that can be suffered by the player in each round is upper bounded. in this paper, we study the worst performance of hedge. |
a simple approach to construct confidence bands for a regression function with incomplete data | a long-standing problem in the construction of asymptotically correct confidence bands for a regression function $m(x)=e[y|x=x]$, where $y$ is the response variable influenced by the covariate $x$, involves the situation where $y$ values may be missing at random, and where the selection probability, the density function $f(x)$ of $x$, and the conditional variance of $y$ given $x$ are all completely unknown. this can be particularly more complicated in nonparametric situations. in this paper we propose a new kernel-type regression estimator and study the limiting distribution of the properly normalized versions of the maximal deviation of the proposed estimator from the true regression curve. the resulting limiting distribution will be used to construct uniform confidence bands for the underlying regression curve with asymptotically correct coverages. the focus of the current paper is on the case where $x\in \mathbb{r}$. we also perform numerical studies to assess the finite-sample performance of the proposed method. in this paper, both mechanics and the theoretical validity of our methods are discussed. |
use dimensionality reduction and svm methods to increase the penetration rate of computer networks | in the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. svm is one of the major algorithms for intrusion detection. in this research, we studied a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction.thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. the main question raised is how we can identify attacks on computer networks with the above-mentioned method. in anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. the network intrusion detection system is used by anomaly detection method that uses the svm algorithm for classification and svd to reduce the size. steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation.the nsl-kdd data set has been used to teach and test the proposed model. in this study, we inferred the intrusion detection using the svm algorithm for classification and svd for diminishing dimensions with no classification algorithm.also the knn algorithm has been compared in situations with and without diminishing dimensions,the results have shown that the proposed method has a better performance than comparable methods. |
metcc: metric learning for confounder control making distance matter in high dimensional biological analysis | high-dimensional data acquired from biological experiments such as next generation sequencing are subject to a number of confounding effects. these effects include both technical effects, such as variation across batches from instrument noise or sample processing, or institution-specific differences in sample acquisition and physical handling, as well as biological effects arising from true but irrelevant differences in the biology of each sample, such as age biases in diseases. prior work has used linear methods to adjust for such batch effects. here, we apply contrastive metric learning by a non-linear triplet network to optimize the ability to distinguish biologically distinct sample classes in the presence of irrelevant technical and biological variation. using whole-genome cell-free dna data from 817 patients, we demonstrate that our approach, metric learning for confounder control (metcc), is able to match or exceed the classification performance achieved using a best-in-class linear method (hcp) or no normalization. critically, results from metcc appear less confounded by irrelevant technical variables like institution and batch than those from other methods even without access to high quality metadata information required by many existing techniques; offering hope for improved generalization. |
deep-rbf networks revisited: robust classification with rejection | one of the main drawbacks of deep neural networks, like many other classifiers, is their vulnerability to adversarial attacks. an important reason for their vulnerability is assigning high confidence to regions with few or even no feature points. by feature points, we mean a nonlinear transformation of the input space extracting a meaningful representation of the input data. on the other hand, deep-rbf networks assign high confidence only to the regions containing enough feature points, but they have been discounted due to the widely-held belief that they have the vanishing gradient problem. in this paper, we revisit the deep-rbf networks by first giving a general formulation for them, and then proposing a family of cost functions thereof inspired by metric learning. in the proposed deep-rbf learning algorithm, the vanishing gradient problem does not occur. we make these networks robust to adversarial attack by adding the reject option to their output layer. through several experiments on the mnist dataset, we demonstrate that our proposed method not only achieves significant classification accuracy but is also very resistant to various adversarial attacks. |
on the lengths of $t$-based confidence intervals | given $n=mk$ $iid$ samples from $n(\theta,\sigma^2)$ with $\theta$ and $\sigma^2$ unknown, we have two ways to construct $t$-based confidence intervals for $\theta$. the traditional method is to treat these $n$ samples as $n$ groups and calculate the intervals. the second, and less frequently used, method is to divide them into $m$ groups with each group containing $k$ elements. for this method, we calculate the mean of each group, and these $k$ mean values can be treated as $iid$ samples from $n(\theta,\sigma^2/k)$. we can use these $k$ values to construct $t$-based confidence intervals. intuition tells us that, at the same confidence level $1-\alpha$, the first method should be better than the second one. yet if we define "better" in terms of the expected length of the confidence interval, then the second method is better because the expected length of the confidence interval obtained from the first method is shorter than the one obtained from the second method. our work proves this intuition theoretically. we also specify that when the elements in each group are correlated, the first method becomes an invalid method, while the second method can give us correct results. we illustrate this with analytical expressions. |
improved knowledge graph embedding using background taxonomic information | knowledge graphs are used to represent relational information in terms of triples. to enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. often there is background taxonomic information (in terms of subclasses and subproperties) that should also be taken into account. we show that existing fully expressive (a.k.a. universal) models cannot provably respect subclass and subproperty information. we show that minimal modifications to an existing knowledge graph completion method enables injection of taxonomic information. moreover, we prove that our model is fully expressive, assuming a lower-bound on the size of the embeddings. experimental results on public knowledge graphs show that despite its simplicity our approach is surprisingly effective. |
higher-order accurate spectral density estimation of functional time series | under the frequency domain framework for weakly dependent functional time series, a key element is the spectral density kernel which encapsulates the second-order dynamics of the process. we propose a class of spectral density kernel estimators based on the notion of a flat-top kernel. the new class of estimators employs the inverse fourier transform of a flat-top function as the weight function employed to smooth the periodogram. it is shown that using a flat-top kernel yields a bias reduction and results in a higher-order accuracy in terms of optimizing the integrated mean square error (imse). notably, the higher-order accuracy of flat-top estimation comes at the sacrifice of the positive semi-definite property. nevertheless, we show how a flat-top estimator can be modified to become positive semi-definite (even strictly positive definite) in finite samples while retaining its favorable asymptotic properties. in addition, we introduce a data-driven bandwidth selection procedure realized by an automatic inspection of the estimated correlation structure. our asymptotic results are complemented by a finite-sample simulation where the higher-order accuracy of flat-top estimators is manifested in practice. |
political popularity analysis in social media | popularity is a critical success factor for a politician and her/his party to win in elections and implement their plans. finding the reasons behind the popularity can provide a stable political movement. this research attempts to measure popularity in twitter using a mixed method. in recent years, twitter data has provided an excellent opportunity for exploring public opinions by analyzing a large number of tweets. this study has collected and examined 4.5 million tweets related to a us politician, senator bernie sanders. this study investigated eight economic reasons behind the senator's popularity in twitter. this research has benefits for politicians, informatics experts, and policymakers to explore public opinion. the collected data will also be available for further investigation. |
an exploratory study of (#)exercise in the twittersphere | social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. this study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. based on the findings, 86% of the detected topics were identified as meaningful topics after conducting the data annotation process. the most discussed exercise-related topics were physical activity (18.7%), lifestyle behaviors (6.6%), and dieting (4%). the results from our experiment indicate that the exploratory data analysis is a practical approach to summarizing the various characteristics of text data for different health and medical applications. |
generalized batch normalization: towards accelerating deep neural networks | utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of batch normalization (bn) that offers accelerated convergence of neural network training compared to conventional bn. in general, we show that mean and standard deviation are not always the most appropriate choice for the centering and scaling procedure within the bn transformation, particularly if relu follows the normalization step. we present a generalized batch normalization (gbn) transformation, which can utilize a variety of alternative deviation measures for scaling and statistics for centering, choices which naturally arise from the theory of generalized deviation measures and risk theory in general. when used in conjunction with the relu non-linearity, the underlying risk theory suggests natural, arguably optimal choices for the deviation measure and statistic. utilizing the suggested deviation measure and statistic, we show experimentally that training is accelerated more so than with conventional bn, often with improved error rate as well. overall, we propose a more flexible bn transformation supported by a complimentary theoretical framework that can potentially guide design choices. |
no peek: a survey of private distributed deep learning | we survey distributed deep learning models for training or inference without accessing raw data from clients. these methods aim to protect confidential patterns in data while still allowing servers to train models. the distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy, homomorphic encryption, oblivious transfer and garbled circuits in the context of neural networks. we study their benefits, limitations and trade-offs with regards to computational resources, data leakage and communication efficiency and also share our anticipated future trends. |
generalization of the pairwise stochastic precedence order to the sequence of random variables | we discuss a new stochastic ordering for the sequence of independent random variables. it generalizes the stochastic precedence order that is defined for two random variables to the case $n>2$. all conventional stochastic orders are transitive, whereas the stochastic precedence order is not. therefore, a new approach to compare the sequence of random variables had to be developed that resulted in the notion of the sequential precedence order. a sufficient condition for this order is derived and some examples are considered. |
adaptive and calibrated ensemble learning with dependent tail-free process | ensemble learning is a mainstay in modern data science practice. conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do not fully account for variations in base model accuracy across subgroups, nor (2) provide uncertainty estimates for the ensemble prediction, which could result in mis-calibrated (i.e. precise but biased) predictions that could in turn negatively impact the algorithm performance in real-word applications. in this work, we present an adaptive, probabilistic approach to ensemble learning using dependent tail-free process as ensemble weight prior. given input feature $\mathbf{x}$, our method optimally combines base models based on their predictive accuracy in the feature space $\mathbf{x} \in \mathcal{x}$, and provides interpretable uncertainty estimates both in model selection and in ensemble prediction. to encourage scalable and calibrated inference, we derive a structured variational inference algorithm that jointly minimize kl objective and the model's calibration score (i.e. continuous ranked probability score (crps)). we illustrate the utility of our method on both a synthetic nonlinear function regression task, and on the real-world application of spatio-temporal integration of particle pollution prediction models in new england. |
neuromodulated learning in deep neural networks | in the brain, learning signals change over time and synaptic location, and are applied based on the learning history at the synapse, in the complex process of neuromodulation. learning in artificial neural networks, on the other hand, is shaped by hyper-parameters set before learning starts, which remain static throughout learning, and which are uniform for the entire network. in this work, we propose a method of deep artificial neuromodulation which applies the concepts of biological neuromodulation to stochastic gradient descent. evolved neuromodulatory dynamics modify learning parameters at each layer in a deep neural network over the course of the network's training. we show that the same neuromodulatory dynamics can be applied to different models and can scale to new problems not encountered during evolution. finally, we examine the evolved neuromodulation, showing that evolution found dynamic, location-specific learning strategies. |
learning montezuma's revenge from a single demonstration | we propose a new method for learning from a single demonstration to solve hard exploration tasks like the atari game montezuma's revenge. instead of imitating human demonstrations, as proposed in other recent works, our approach is to maximize rewards directly. our agent is trained using off-the-shelf reinforcement learning, but starts every episode by resetting to a state from a demonstration. by starting from such demonstration states, the agent requires much less exploration to learn a game compared to when it starts from the beginning of the game at every episode. we analyze reinforcement learning for tasks with sparse rewards in a simple toy environment, where we show that the run-time of standard rl methods scales exponentially in the number of states between rewards. our method reduces this to quadratic scaling, opening up many tasks that were previously infeasible. we then apply our method to montezuma's revenge, for which we present a trained agent achieving a high-score of 74,500, better than any previously published result. |
learning graph representation via formal concept analysis | we present a novel method that can learn a graph representation from multivariate data. in our representation, each node represents a cluster of data points and each edge represents the subset-superset relationship between clusters, which can be mutually overlapped. the key to our method is to use formal concept analysis (fca), which can extract hierarchical relationships between clusters based on the algebraic closedness property. we empirically show that our method can effectively extract hierarchical structures of clusters compared to the baseline method. |
efficient transfer learning and online adaptation with latent variable models for continuous control | traditional model-based rl relies on hand-specified or learned models of transition dynamics of the environment. these methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. we propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. we use online bayesian inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task halfcheetah. |
autogan: robust classifier against adversarial attacks | classifiers fail to classify correctly input images that have been purposefully and imperceptibly perturbed to cause misclassification. this susceptability has been shown to be consistent across classifiers, regardless of their type, architecture or parameters. common defenses against adversarial attacks modify the classifer boundary by training on additional adversarial examples created in various ways. in this paper, we introduce autogan, which counters adversarial attacks by enhancing the lower-dimensional manifold defined by the training data and by projecting perturbed data points onto it. autogan mitigates the need for knowing the attack type and magnitude as well as the need for having adversarial samples of the attack. our approach uses a generative adversarial network (gan) with an autoencoder generator and a discriminator that also serves as a classifier. we test autogan against adversarial samples generated with state-of-the-art fast gradient sign method (fgsm) as well as samples generated with random gaussian noise, both using the mnist dataset. for different magnitudes of perturbation in training and testing, autogan can surpass the accuracy of fgsm method by up to 25\% points on samples perturbed using fgsm. without an augmented training dataset, autogan achieves an accuracy of 89\% compared to 1\% achieved by fgsm method on fgsm testing adversarial samples. |
binary input layer: training of cnn models with binary input data | for the efficient execution of deep convolutional neural networks (cnn) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. binarization of the first layer was always excluded, as it leads to a significant error increase. here, we present the novel concept of binary input layer (bil), which allows the usage of binary input data by learning bit specific binary weights. the concept is evaluated on three datasets (pamap2, svhn, cifar-10). our results show that this approach is in particular beneficial for multimodal datasets (pamap2) where it outperforms networks using full precision weights in the first layer by 1:92 percentage points (pp) while consuming only 2 % of the chip area. |
zero initialization of modified gated recurrent encoder-decoder network for short term load forecasting | single layer feedforward neural network(fnn) is used many a time as a last layer in models such as seq2seq or could be a simple rnn network. the importance of such layer is to transform the output to our required dimensions. when it comes to weights and biases initialization, there is no such specific technique that could speed up the learning process. we could depend on deep network initialization techniques such as xavier or he initialization. but such initialization fails to show much improvement in learning speed or accuracy. in this paper we propose zero initialization (zi) for weights of a single layer network. we first test this technique with on a simple rnn network and compare the results against xavier, he and identity initialization. as a final test we implement it on a seq2seq network. it was found that zi considerably reduces the number of epochs used and improve the accuracy. the developed model has been applied for short-term load forecasting using the load data of australian energy market. the model is able to forecast the day ahead load accurately with error of 0.94%. |
bootstrapping f test for testing random effects in linear mixed models | recently hui et al. (2018) use f tests for testing a subset of random effect, demonstrating its computational simplicity and exactness when the first two moment of the random effects are specified. we extended the investigation of the f test in the following two aspects: firstly, we examined the power of the f test under non-normality of the errors. secondly, we consider bootstrap counterparts to the f test, which offer improvement for the cases with small cluster size or for the cases with non-normal errors. |
constant versus covariate dependent threshold in the peaks-over threshold method | the peaks-over threshold is a fundamental method in the estimation of rare events such as small exceedance probabilities, extreme quantiles and return periods. the main problem with the peaks-over threshold method relates to the selection of threshold above and below which the asymptotic results are valid for large and small observations respectively. in addition, the main assumption leading to the asymptotic results is that the observations are independent and identically distributed. however, in practice, many real life processes yield data that are non-stationary and/or related to some covariate variables. as a result, threshold selection gets complicated as it may depend on the covariates. strong arguments have been made against the use of constant threshold as observation that is considered extreme at some covariate level may not qualify as an extreme observation at another covariate level. some authors have attempted to obtain covariate dependent thresholds in different ways: the most appealing one relies on quantile regression. in this paper, we propose a covariate dependent threshold based on expectiles. we compare this threshold with the constant and the quantile regression in a simulation study for estimating the tail index of the generalised pareto distribution. as may be expected, no threshold is universally the best. however, certain general observations can be made for the exponential growth data considered. firstly, we find that the expectile threshold outperforms the others when the response variable has smaller to medium values. secondly, for larger values of the response variable, the constant threshold is generally the best method. the threshold selection methods are illustrated in the estimation of the tail index of an insurance claims data. |
on the estimation of the lorenz curve under complex sampling designs | this paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with different inclusion probabilities. a (design-based) hajek type estimator for the lorenz curve is proposed, and its asymptotic properties are studied. then, a resampling scheme able to approximate the asymptotic law of the lorenz curve estimator is constructed. applications are given to the construction of (i) a confidence band for the lorenz curve, (ii) confidence intervals for the gini concentration ratio, and (iii) a test for lorenz dominance. the merits of the proposed resampling procedure are evaluated through a simulation study. |
a matching based clustering algorithm for categorical data | cluster analysis is one of the essential tasks in data mining and knowledge discovery. each type of data poses unique challenges in achieving relatively efficient partitioning of the data into homogeneous groups. while the algorithms for numeric data are relatively well studied in the literature, there are still challenges to address in case of categorical data. the main issue is the unordered structure of categorical data, which makes the implementation of the standard concepts of clustering algorithms difficult. for instance, the assessment of distance between objects, the selection of representatives for categorical data is not as straightforward as for continuous data. therefore, this paper presents a new framework for partitioning categorical data, which does not use the distance measure as a key concept. the matching based clustering algorithm is designed based on the similarity matrix and a framework for updating the latter using the feature importance criteria. the experimental results show this algorithm can serve as an alternative to existing ones and can be an efficient knowledge discovery tool. |
physics-informed deep generative models | we consider the application of deep generative models in propagating uncertainty through complex physical systems. specifically, we put forth an implicit variational inference formulation that constrains the generative model output to satisfy given physical laws expressed by partial differential equations. such physics-informed constraints provide a regularization mechanism for effectively training deep probabilistic models for modeling physical systems in which the cost of data acquisition is high and training data-sets are typically small. this provides a scalable framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations. we demonstrate the effectiveness of our approach through a canonical example in transport dynamics. |
spatio-temporal models for big multinomial data using the conditional multivariate logit-beta distribution | we introduce a bayesian approach for analyzing high-dimensional multinomial data that are referenced over space and time. in particular, the proportions associated with multinomial data are assumed to have a logit link to a latent spatio-temporal mixed effects model. this strategy allows for covariances that are nonstationarity in both space and time, asymmetric, and parsimonious. we also introduce the use of the conditional multivariate logit-beta distribution into the dependent multinomial data setting, which leads to conjugate full-conditional distributions for use in a collapsed gibbs sampler. we refer to this model as the multinomial spatio-temporal mixed effects model (mn-stm). additionally, we provide methodological developments including: the derivation of the associated full-conditional distributions, a relationship with a latent gaussian process model, and the stability of the non-stationary vector autoregressive model. we illustrate the mn-stm through simulations and through a demonstration with public-use quarterly workforce indicators (qwi) data from the longitudinal employer household dynamics (lehd) program of the u.s. census bureau. |
closed-form inference and prediction in gaussian process state-space models | we examine an analytic variational inference scheme for the gaussian process state space model (gpssm) - a probabilistic model for system identification and time-series modelling. our approach performs variational inference over both the system states and the transition function. we exploit markov structure in the true posterior, as well as an inducing point approximation to achieve linear time complexity in the length of the time series. contrary to previous approaches, no monte carlo sampling is required: inference is cast as a deterministic optimisation problem. in a number of experiments, we demonstrate the ability to model non-linear dynamics in the presence of both process and observation noise as well as to impute missing information (e.g. velocities from raw positions through time), to de-noise, and to estimate the underlying dimensionality of the system. finally, we also introduce a closed-form method for multi-step prediction, and a novel criterion for assessing the quality of our approximate posterior. |
statement networks: a power structure narrative as depicted by newspapers | we report a data mining pipeline and subsequent analysis to understand the core periphery power structure created in three national newspapers in bangladesh, as depicted by statements made by people appearing in news. statements made by one actor about another actor can be considered a form of public conversation. named entity recognition techniques can be used to create a temporal actor network from such conversations, which shows some unique structure, and reveals much room for improvement in news reporting and also the top actors' conversation preferences. our results indicate there is a presence of cliquishness between powerful political leaders when it comes to their appearance in news. we also show how these cohesive cores form through the news articles, and how, over a decade, news cycles change the actors belonging in these groups. |
post-selection inference for changepoint detection algorithms with application to copy number variation data | changepoint detection methods are used in many areas of science and engineering, e.g., in the analysis of copy number variation data, to detect abnormalities in copy numbers along the genome. despite the broad array of available tools, methodology for quantifying our uncertainty in the strength (or presence) of given changepoints, post-detection, are lacking. post-selection inference offers a framework to fill this gap, but the most straightforward application of these methods results in low-powered tests and leaves open several important questions about practical usability. in this work, we carefully tailor post-selection inference methods towards changepoint detection, focusing as our main scientific application on copy number variation data. as for changepoint algorithms, we study binary segmentation, and two of its most popular variants, wild and circular, and the fused lasso. we implement some of the latest developments in post-selection inference theory: we use auxiliary randomization to improve power, which requires implementations of mcmc algorithms (importance sampling and hit-and-run sampling) to carry out our tests. we also provide recommendations for improving practical useability, detailed simulations, and an example analysis on array comparative genomic hybridization (cgh) data. |
testing for high-dimensional network parameters in auto-regressive models | high-dimensional auto-regressive models provide a natural way to model influence between $m$ actors given multi-variate time series data for $t$ time intervals. while there has been considerable work on network estimation, there is limited work in the context of inference and hypothesis testing. in particular, prior work on hypothesis testing in time series has been restricted to linear gaussian auto-regressive models. from a practical perspective, it is important to determine suitable statistical tests for connections between actors that go beyond the gaussian assumption. in the context of \emph{high-dimensional} time series models, confidence intervals present additional estimators since most estimators such as the lasso and dantzig selectors are biased which has led to \emph{de-biased} estimators. in this paper we address these challenges and provide convergence in distribution results and confidence intervals for the multi-variate ar(p) model with sub-gaussian noise, a generalization of gaussian noise that broadens applicability and presents numerous technical challenges. the main technical challenge lies in the fact that unlike gaussian random vectors, for sub-gaussian vectors zero correlation does not imply independence. the proof relies on using an intricate truncation argument to develop novel concentration bounds for quadratic forms of dependent sub-gaussian random variables. our convergence in distribution results hold provided $t = \omega((s \vee \rho)^2 \log^2 m)$, where $s$ and $\rho$ refer to sparsity parameters which matches existed results for hypothesis testing with i.i.d. samples. we validate our theoretical results with simulation results for both block-structured and chain-structured networks. |
taxi demand-supply forecasting: impact of spatial partitioning on the performance of neural networks | in this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. our study compares (i) the variable-sized polygon based voronoi tessellation, and (ii) the fixed-sized grid based geohash tessellation, using taxi demand-supply gps data for the cities of bengaluru, india and new york, usa. long short-term memory (lstm) networks are used for modeling and incorporating information from spatial neighbors into the model. we find that the lstm model based on input features extracted from a variable-sized polygon tessellation yields superior performance over the lstm model based on fixed-sized grid tessellation. our study highlights the need to explore multiple spatial partitioning techniques for improving the prediction performance in neural network models. |
on the interrelation between dependence coefficients of extreme value copulas | for extreme value copulas with a known upper tail dependence coefficient we find pointwise upper and lower bounds, which are used to establish upper and lower bounds of the spearman and kendall correlation coefficients. we shown that in all cases the lower bounds are attained on marshall--olkin copulas, and the upper ones, on copulas with piecewise linear dependence functions. |
sufficient dimension reduction for classification | we propose a new sufficient dimension reduction approach designed deliberately for high-dimensional classification. this novel method is named maximal mean variance (mmv), inspired by the mean variance index first proposed by cui, li and zhong (2015), which measures the dependence between a categorical random variable with multiple classes and a continuous random variable. our method requires reasonably mild restrictions on the predicting variables and keeps the model-free advantage without the need to estimate the link function. the consistency of the mmv estimator is established under regularity conditions for both fixed and diverging dimension (p) cases and the number of the response classes can also be allowed to diverge with the sample size n. we also construct the asymptotic normality for the estimator when the dimension of the predicting vector is fixed. furthermore, our method works pretty well when n < p. the surprising classification efficiency gain of the proposed method is demonstrated by simulation studies and real data analysis. |
hierarchical bipartite graph convolution networks | recently, graph neural networks have been adopted in a wide variety of applications ranging from relational representations to modeling irregular data domains such as point clouds and social graphs. however, the space of graph neural network architectures remains highly fragmented impeding the development of optimized implementations similar to what is available for convolutional neural networks. in this work, we present bigraphnet, a graph neural network architecture that generalizes many popular graph neural network models and enables new efficient operations similar to those supported by convnets. by explicitly separating the input and output nodes, bigraphnet: (i) generalizes the graph convolution to support new efficient operations such as coarsened graph convolutions (similar to strided convolution in convnets), multiple input graphs convolution and graph expansions (unpooling) which can be used to implement various graph architectures such as graph autoencoders, and graph residual nets; and (ii) accelerates and scales the computations and memory requirements in hierarchical networks by performing computations only at specified output nodes. |
asynchronous training of word embeddings for large text corpora | word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. training embeddings over huge corpora is computationally expensive because the input is typically sequentially processed and parameters are synchronously updated. distributed architectures for asynchronous training that have been proposed either focus on scaling vocabulary sizes and dimensionality or suffer from expensive synchronization latencies. in this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings. our training procedure does not involve any parameter synchronization except a final sub-model merge phase that typically executes in a few minutes. our distributed training scales seamlessly to large corpus sizes and we get comparable and sometimes even up to 45% performance improvement in a variety of nlp benchmarks using models trained by our distributed procedure which requires $1/10$ of the time taken by the baseline approach. finally we also show that we are robust to missing words in sub-models and are able to effectively reconstruct word representations. |
non-intrusive load monitoring with fully convolutional networks | non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. deep neural networks have been shown to be effective for energy disaggregation. in this work, we present a deep neural network architecture which achieves state of the art disaggregation performance with substantially improved computational efficiency, reducing model training time by a factor of 32 and prediction time by a factor of 43. this improvement in efficiency could be especially useful for applications where disaggregation must be performed in home on lower power devices, or for research experiments which involve training a large number of models. |
rapid prototyping model for healthcare alternative payment models: replicating the federally qualified health center advanced primary care practice demonstration | innovation in healthcare payment and service delivery utilizes high cost, high risk pilots paired with traditional program evaluations. decision-makers are unable to reliably forecast the impacts of pilot interventions in this complex system, complicating the feasibility assessment of proposed healthcare models. we developed and validated a discrete event simulation (des) model of primary care for patients with diabetes to allow rapid prototyping and assessment of models before pilot implementation. we replicated four outcomes from the centers for medicare and medicaid services federally qualified health center advanced primary care practice pilot. the des model simulates a synthetic population's healthcare experience, including symptom onset, appointment scheduling, screening, and treatment, as well as the impact of physician training. a network of detailed event modules was developed from peer-reviewed literature. synthetic patients' attributes modify the probability distributions for event outputs and direct them through an episode of care; attributes are in turn modified by patients' experiences. our model replicates the direction of the effect of physician training on the selected outcomes, and the strength of the effect increases with the number of trainings. the simulated effect strength replicates the pilot results for eye exams and nephropathy screening, but over-estimates results for hba1c and ldl screening. our model will improve decision-makers' abilities to assess the feasibility of pilot success, with reproducible, literature-based systems models. our model identifies intervention and healthcare system components to which outcomes are sensitive, so these aspects can be monitored and controlled during pilot implementation. more work is needed to improve replication of hba1c and ldl screening, and to elaborate sub-models related to intervention components. |
improving model-based control and active exploration with reconstruction uncertainty optimization | model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable. in this work, we propose a model agnostic method for estimating the uncertainty of a model?s predictions based on reconstruction error, using it in control and exploration. as our experiments show, this uncertainty estimation can be used to improve control performance on a wide variety of environments by choosing predictions of which the model is confident. it can also be used for active learning to explore more efficiently the environment by planning for trajectories with high uncertainty, allowing faster model learning. |
disentangled dynamic representations from unordered data | we present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. our approach exploits regularities in sequential data that exist regardless of the order in which the data is viewed. the result of our factorized graphical model is a well-organized and coherent latent space for data dynamics. we demonstrate our method on several synthetic dynamic datasets and real video data featuring various facial expressions and head poses. |
guided dropout | dropout is often used in deep neural networks to prevent over-fitting. conventionally, dropout training invokes \textit{random drop} of nodes from the hidden layers of a neural network. it is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout. in this research, we propose "guided dropout" for training deep neural network which drop nodes by measuring the strength of each node. we also demonstrate that conventional dropout is a specific case of the proposed guided dropout. experimental evaluation on multiple datasets including mnist, cifar10, cifar100, svhn, and tiny imagenet demonstrate the efficacy of the proposed guided dropout. |
theoretical analysis of auto rate-tuning by batch normalization | batch normalization (bn) has become a cornerstone of deep learning across diverse architectures, appearing to help optimization as well as generalization. while the idea makes intuitive sense, theoretical analysis of its effectiveness has been lacking. here theoretical support is provided for one of its conjectured properties, namely, the ability to allow gradient descent to succeed with less tuning of learning rates. it is shown that even if we fix the learning rate of scale-invariant parameters (e.g., weights of each layer with bn) to a constant (say, $0.3$), gradient descent still approaches a stationary point (i.e., a solution where gradient is zero) in the rate of $t^{-1/2}$ in $t$ iterations, asymptotically matching the best bound for gradient descent with well-tuned learning rates. a similar result with convergence rate $t^{-1/4}$ is also shown for stochastic gradient descent. |
dosed: a deep learning approach to detect multiple sleep micro-events in eeg signal | background: electroencephalography (eeg) monitors brain activity during sleep and is used to identify sleep disorders. in sleep medicine, clinicians interpret raw eeg signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. for diagnosis, they also rely on shorter prototypical micro-architecture events which exhibit variable durations and shapes, such as spindles, k-complexes or arousals. annotating such events is traditionally performed by a trained sleep expert, making the process time consuming, tedious and subject to inter-scorer variability. to automate this procedure, various methods have been developed, yet these are event-specific and rely on the extraction of hand-crafted features. new method: we propose a novel deep learning architecure called dreem one shot event detector (dosed). dosed jointly predicts locations, durations and types of events in eeg time series. the proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as yolo and ssd. it relies on a convolutional neural network that builds a feature representation from raw eeg signals, as well as two modules performing localization and classification respectively. results and comparison with other methods: the proposed approach is tested on 4 datasets and 3 types of events (spindles, k-complexes, arousals) and compared to the current state-of-the-art detection algorithms. conclusions: results demonstrate the versatility of this new approach and improved performance compared to the current state-of-the-art detection methods. |
top-n-rank: a scalable list-wise ranking method for recommender systems | we propose top-n-rank, a novel family of list-wise learning-to-rank models for reliably recommending the n top-ranked items. the proposed models optimize a variant of the widely used discounted cumulative gain (dcg) objective function which differs from dcg in two important aspects: (i) it limits the evaluation of dcg only on the top n items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function; and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustworthiness. because the resulting objective function is non-smooth and hence challenging to optimize, we consider two smooth approximations of the objective function, using the traditional sigmoid function and the rectified linear unit (relu). we propose a family of learning-to-rank algorithms (top-n-rank) that work with any smooth objective function. then, a more efficient variant, top-n-rank.relu, is introduced, which effectively exploits the properties of relu function to reduce the computational complexity of top-n-rank from quadratic to linear in the average number of items rated by users. the results of our experiments using two widely used benchmarks, namely, the movielens data set and the amazon video games data set demonstrate that: (i) the `top-n truncation' of the objective function substantially improves the ranking quality of the top n recommendations; (ii) using the relu for smoothing the objective function yields significant improvement in both ranking quality as well as runtime as compared to using the sigmoid; and (iii) top-n-rank.relu substantially outperforms the well-performing list-wise ranking methods in terms of ranking quality. |
montage based 3d medical image retrieval from traumatic brain injury cohort using deep convolutional neural network | brain imaging analysis on clinically acquired computed tomography (ct) is essential for the diagnosis, risk prediction of progression, and treatment of the structural phenotypes of traumatic brain injury (tbi). however, in real clinical imaging scenarios, entire body ct images (e.g., neck, abdomen, chest, pelvis) are typically captured along with whole brain ct scans. for instance, in a typical sample of clinical tbi imaging cohort, only ~15% of ct scans actually contain whole brain ct images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images. therefore, a manual image retrieval process is typically required to isolate the whole brain ct scans from the entire cohort. however, the manual image retrieval is time and resource consuming and even more difficult for the larger cohorts. to alleviate the manual efforts, in this paper we propose an automated 3d medical image retrieval pipeline, called deep montage-based image retrieval (dmir), which performs classification on 2d montage images via a deep convolutional neural network. the novelty of the proposed method for image processing is to characterize the medical image retrieval task based on the montage images. in a cohort of 2000 clinically acquired tbi scans, 794 scans were used as training data, 206 scans were used as validation data, and the remaining 1000 scans were used as testing data. the proposed achieved accuracy=1.0, recall=1.0, precision=1.0, f1=1.0 for validation data, while achieved accuracy=0.988, recall=0.962, precision=0.962, f1=0.962 for testing data. thus, the proposed dmir is able to perform accurate ct whole brain image retrieval from large-scale clinical cohorts. |
learning sharing behaviors with arbitrary numbers of agents | we propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. we model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of agents. the individual behavior models are weighted finite state transducers (wfsts) with weights dynamically updated during interactions, and the multi-agent fusion model is a logistic regression classifier. we test our models in a multi-agent tower-building environment, where a q-learning agent learns to interact with rule-based agents. our approach accurately models the underlying behavior patterns of the rule-based agents with accuracy ranging between 0.63 and 1.0 depending on the stochasticity of the other agent behaviors. in addition we show using kl-divergence that the model accurately captures the distribution of next actions when interacting with both a single agent (kl-divergence < 0.1) and with multiple agents (kl-divergence < 0.37). finally, we demonstrate that our behavior model can be used by a q-learning agent to take turns in an interactive turn-taking environment. |
duelling bandits with weak regret in adversarial environments | research on the multi-armed bandit problem has studied the trade-off of exploration and exploitation in depth. however, there are numerous applications where the cardinal absolute-valued feedback model (e.g. ratings from one to five) is not suitable. this has motivated the formulation of the duelling bandits problem, where the learner picks a pair of actions and observes a noisy binary feedback, indicating a relative preference between the two. there exist a multitude of different settings and interpretations of the problem for two reasons. first, due to the absence of a total order of actions, there is no natural definition of the best action. existing work either explicitly assumes the existence of a linear order, or uses a custom definition for the winner. second, there are multiple reasonable notions of regret to measure the learner's performance. most prior work has been focussing on the $\textit{strong regret}$, which averages the quality of the two actions picked. this work focusses on the $\textit{weak regret}$, which is based on the quality of the better of the two actions selected. weak regret is the more appropriate performance measure when the pair's inferior action has no significant detrimental effect on the pair's quality. we study the duelling bandits problem in the adversarial setting. we provide an algorithm which has theoretical guarantees in both the utility-based setting, which implies a total order, and the unrestricted setting. for the latter, we work with the $\textit{borda winner}$, finding the action maximising the probability of winning against an action sampled uniformly at random. the thesis concludes with experimental results based on both real-world data and synthetic data, showing the algorithm's performance and limitations. |
kf-lax: kronecker-factored curvature estimation for control variate optimization in reinforcement learning | a key challenge for gradient based optimization methods in model-free reinforcement learning is to develop an approach that is sample efficient and has low variance. in this work, we apply kronecker-factored curvature estimation technique (kfac) to a recently proposed gradient estimator for control variate optimization, relax, to increase the sample efficiency of using this gradient estimation method in reinforcement learning. the performance of the proposed method is demonstrated on a synthetic problem and a set of three discrete control task atari games. |
dynamic sparse factor analysis | its conceptual appeal and effectiveness has made latent factor modeling an indispensable tool for multivariate analysis. despite its popularity across many fields, there are outstanding methodological challenges that have hampered practical deployments. one major challenge is the selection of the number of factors, which is exacerbated for dynamic factor models, where factors can disappear, emerge, and/or reoccur over time. existing tools that assume a fixed number of factors may provide a misguided representation of the data mechanism, especially when the number of factors is crudely misspecified. another challenge is the interpretability of the factor structure, which is often regarded as an unattainable objective due to the lack of identifiability. motivated by a topical macroeconomic application, we develop a flexible bayesian method for dynamic factor analysis (dfa) that can simultaneously accommodate a time-varying number of factors and enhance interpretability without strict identifiability constraints. to this end, we turn to dynamic sparsity by employing dynamic spike-and-slab (dss) priors within dfa. scalable bayesian em estimation is proposed for fast posterior mode identification via rotations to sparsity, enabling bayesian data analysis at scales that would have been previously time-consuming. we study a large-scale balanced panel of macroeconomic variables covering multiple facets of the us economy, with a focus on the great recession, to highlight the efficacy and usefulness of our proposed method. |
new approaches to inverse structural modification theory using random projections | in many contexts the modal properties of a structure change, either due to the impact of a changing environment, fatigue, or due to the presence of structural damage. for example during flight, an aircraft's modal properties are known to change with both altitude and velocity. it is thus important to quantify these changes given only a truncated set of modal data, which is usually the case experimentally. this procedure is formally known as the generalised inverse eigenvalue problem. in this paper we experimentally show that first-order gradient-based methods that optimise objective functions defined over a modal are prohibitive due to the required small step sizes. this in turn leads to the justification of using a non-gradient, black box optimiser in the form of particle swarm optimisation. we further show how it is possible to solve such inverse eigenvalue problems in a lower dimensional space by the use of random projections, which in many cases reduces the total dimensionality of the optimisation problem by 80% to 99%. two example problems are explored involving a ten-dimensional mass-stiffness toy problem, and a one-dimensional finite element mass-stiffness approximation for a boeing 737-300 aircraft. |
on the dimensionality of word embedding | in this paper, we provide a theoretical understanding of word embedding and its dimensionality. motivated by the unitary-invariance of word embedding, we propose the pairwise inner product (pip) loss, a novel metric on the dissimilarity between word embeddings. using techniques from matrix perturbation theory, we reveal a fundamental bias-variance trade-off in dimensionality selection for word embeddings. this bias-variance trade-off sheds light on many empirical observations which were previously unexplained, for example the existence of an optimal dimensionality. moreover, new insights and discoveries, like when and how word embeddings are robust to over-fitting, are revealed. by optimizing over the bias-variance trade-off of the pip loss, we can explicitly answer the open question of dimensionality selection for word embedding. |
learning what to remember: long-term episodic memory networks for learning from streaming data | current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. one example of this is lifelong learning scenario where the model receives unlimited length of data stream as an input which contains vast majority of uninformative entries. we tackle this problem by proposing a memory network fit for long-term lifelong learning scenario, which we refer to as long-term episodic memory networks (lemn), that features a rnn-based retention agent that learns to replace less important memory entries based on the retention probability generated on each entry that is learned to identify data instances of generic importance relative to other memory entries, as well as its historical importance. such learning of retention agent allows our long-term episodic memory network to retain memory entries of generic importance for a given task. we validate our model on a path-finding task as well as synthetic and real question answering tasks, on which our model achieves significant improvements over the memory augmented networks with rule-based memory scheduling as well as an rl-based baseline that does not consider relative or historical importance of the memory. |
distribution-free properties of isotonic regression | it is well known that the isotonic least squares estimator is characterized as the derivative of the greatest convex minorant of a random walk. provided the walk has exchangeable increments, we prove that the slopes of the greatest convex minorant are distributed as order statistics of the running averages. this result implies an exact non-asymptotic formula for the squared error risk of least squares in isotonic regression when the true sequence is constant that holds for every exchangeable error distribution. |
deep density-based image clustering | recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. however, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks. in addition, the initial cluster centers in the learned feature space are generated by $k$-means. this only works well on spherical clusters and probably leads to unstable clustering results. in this paper, we propose a two-stage deep density-based image clustering (ddc) framework to address these issues. the first stage is to train a deep convolutional autoencoder (cae) to extract low-dimensional feature representations from high-dimensional image data, and then apply t-sne to further reduce the data to a 2-dimensional space favoring density-based clustering algorithms. the second stage is to apply the developed density-based clustering technique on the 2-dimensional embedded data to automatically recognize an appropriate number of clusters with arbitrary shapes. concretely, a number of local clusters are generated to capture the local structures of clusters, and then are merged via their density relationship to form the final clustering result. experiments demonstrate that the proposed ddc achieves comparable or even better clustering performance than state-of-the-art deep clustering methods, even though the number of clusters is not given. |
bayesian nonparametric model for weighted data using mixture of burr xii distributions | dirichlet process mixture model (dpmm) is a popular bayesian nonparametric model. in this paper, we apply this model to weighted data and then estimate the un-weighted distribution from the corresponding weighted distribution using the metropolis-hastings algorithm. we then apply the dpmm with different kernels to simulated and real data sets. in particular, we work with lifetime data in the presence of censored data and then calculate estimated density and survival values. |
a combined strategy for multivariate density estimation | non-linear aggregation strategies have recently been proposed in response to the problem of how to combine, in a non-linear way, estimators of the regression function (see for instance \cite{biau:16}), classification rules (see \cite{ch:16}), among others. although there are several linear strategies to aggregate density estimators, most of them are hard to compute (even in moderate dimensions). our approach aims to overcome this problem by estimating the density at a point $x$ using not just sample points close to $x$ but in a neighborhood of the (estimated) level set $f(x)$. we show, both theoretically and through a simulation study, that the mean squared error of our proposal is smaller than that of the aggregated densities. a central limit theorem is also proven. |
towards automatic personality prediction using facebook like categories | we demonstrate that effortlessly accessible digital records of behavior such as facebook likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits. the analysis presented based on a dataset of over 738,000 users who conferred their facebook likes, social network activities, egocentric network, demographic characteristics, and the results of various psychometric tests for our extended personality analysis. the proposed model uses unique mapping technique between each facebook like object to the corresponding facebook page category/sub-category object, which is then evaluated as features for a set of machine learning algorithms to predict individual psycho-demographic profiles from likes. the model , distinguishes between a religious and non-religious individual in 83% of circumstances, asian and european in 87% of situations, and between emotional stable and emotion unstable in 81% of situations. we provide exemplars of correlations between attributes and likes and present suggestions for future directions. |
efficient model-free reinforcement learning using gaussian process | efficient reinforcement learning usually takes advantage of demonstration or good exploration strategy. by applying posterior sampling in model-free rl under the hypothesis of gp, we propose gaussian process posterior sampling reinforcement learning(gppstd) algorithm in continuous state space, giving theoretical justifications and empirical results. we also provide theoretical and empirical results that various demonstration could lower expected uncertainty and benefit posterior sampling exploration. in this way, we combined the demonstration and exploration process together to achieve a more efficient reinforcement learning. |
exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes | we introduce and analyse two algorithms for exploration-exploitation in discrete and continuous markov decision processes (mdps) based on exploration bonuses. scal$^+$ is a variant of scal (fruit et al., 2018) that performs efficient exploration-exploitation in any unknown weakly-communicating mdp for which an upper bound c on the span of the optimal bias function is known. for an mdp with $s$ states, $a$ actions and $\gamma \leq s$ possible next states, we prove that scal$^+$ achieves the same theoretical guarantees as scal (i.e., a high probability regret bound of $\widetilde{o}(c\sqrt{\gamma sat})$), with a much smaller computational complexity. similarly, c-scal$^+$ exploits an exploration bonus to achieve sublinear regret in any undiscounted mdp with continuous state space. we show that c-scal$^+$ achieves the same regret bound as uccrl (ortner and ryabko, 2012) while being the first implementable algorithm with regret guarantees in this setting. while optimistic algorithms such as ucrl, scal or uccrl maintain a high-confidence set of plausible mdps around the true unknown mdp, scal$^+$ and c-scal$^+$ leverage on an exploration bonus to directly plan on the empirically estimated mdp, thus being more computationally efficient. |
sparse component separation from poisson measurements | blind source separation (bss) aims at recovering signals from mixtures. this problem has been extensively studied in cases where the mixtures are contaminated with additive gaussian noise. however, it is not well suited to describe data that are corrupted with poisson measurements such as in low photon count optics or in high-energy astronomical imaging (e.g. observations from the chandra or fermi telescopes). to that purpose, we propose a novel bss algorithm coined pgmca that specifically tackles the blind separation of sparse sources from poisson measurements. |
from adaptive kernel density estimation to sparse mixture models | we introduce a balloon estimator in a generalized expectation-maximization method for estimating all parameters of a gaussian mixture model given one data sample per mixture component. instead of limiting explicitly the model size, this regularization strategy yields low-complexity sparse models where the number of effective mixture components reduces with an increase of a smoothing probability parameter $\mathbf{p>0}$. this semi-parametric method bridges from non-parametric adaptive kernel density estimation (kde) to parametric ordinary least-squares when $\mathbf{p=1}$. experiments show that simpler sparse mixture models retain the level of details present in the adaptive kde solution. |
encoding prior knowledge in the structure of the likelihood | the inference of deep hierarchical models is problematic due to strong dependencies between the hierarchies. we investigate a specific transformation of the model parameters based on the multivariate distributional transform. this transformation is a special form of the reparametrization trick, flattens the hierarchy and leads to a standard gaussian prior on all resulting parameters. the transformation also transfers all the prior information into the structure of the likelihood, hereby decoupling the transformed parameters a priori from each other. a variational gaussian approximation in this standardized space will be excellent in situations of relatively uninformative data. additionally, the curvature of the log-posterior is well-conditioned in directions that are weakly constrained by the data, allowing for fast inference in such a scenario. in an example we perform the transformation explicitly for gaussian process regression with a priori unknown correlation structure. deep models are inferred rapidly in highly and slowly in poorly informed situations. the flat model show exactly the opposite performance pattern. a synthesis of both, the deep and the flat perspective, provides their combined advantages and overcomes the individual limitations, leading to a faster inference. |
learning item-interaction embeddings for user recommendations | industry-scale recommendation systems have become a cornerstone of the e-commerce shopping experience. for etsy, an online marketplace with over 50 million handmade and vintage items, users come to rely on personalized recommendations to surface relevant items from its massive inventory. one hallmark of etsy's shopping experience is the multitude of ways in which a user can interact with an item they are interested in: they can view it, favorite it, add it to a collection, add it to cart, purchase it, etc. we hypothesize that the different ways in which a user interacts with an item indicates different kinds of intent. consequently, a user's recommendations should be based not only on the item from their past activity, but also the way in which they interacted with that item. in this paper, we propose a novel method for learning interaction-based item embeddings that encode the co-occurrence patterns of not only the item itself, but also the interaction type. the learned embeddings give us a convenient way of approximating the likelihood that one item-interaction pair would co-occur with another by way of a simple inner product. because of its computational efficiency, our model lends itself naturally as a candidate set selection method, and we evaluate it as such in an industry-scale recommendation system that serves live traffic on etsy.com. our experiments reveal that taking interaction type into account shows promising results in improving the accuracy of modeling user shopping behavior. |
synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction | in our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of smiles. the model we propose, the so-called convolutional neural fingerprint (cnf) model, reaches the accuracy of traditional descriptors such as dragon (mauri et al. [22]), rdkit (landrum [18]), cdk2 (willighagen et al. [43]) and pydescriptor (masand and rastija [20]). moreover the cnf model generally performs better than highly fine-tuned traditional descriptors, especially on small data sets, which is of great interest for the chemical field where data sets are generally small due to experimental costs, the availability of molecules or accessibility to private databases. we evaluate the cnf model along with smiles augmentation during both training and testing. to the best of our knowledge, this is the first time that such a methodology is presented. we show that using the multiplicity of smiles during training acts as a regulariser and therefore avoids overfitting and can be seen as ensemble learning when considered for testing. |
data strategies for fleetwide predictive maintenance | for predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements, and sensor inputs. to simplify the time and accuracy comparison between 27 different algorithms, we treat the imbalance between normal and failing states with nominal under-sampling. we identify 3 promising regression and discriminant algorithms with both higher accuracy (96%) and twenty-fold faster execution times than previous work. because predictive maintenance success hinges on input features prior to prediction, we provide a methodology to rank-order feature importance and show that for this dataset, error counts prove more predictive than scheduled maintenance might imply solely based on more traditional factors such as machine age or last replacement times. |
seq2graph: discovering dynamic dependencies from multivariate time series with multi-level attention | discovering temporal lagged and inter-dependencies in multivariate time series data is an important task. however, in many real-world applications, such as commercial cloud management, manufacturing predictive maintenance, and portfolios performance analysis, such dependencies can be non-linear and time-variant, which makes it more challenging to extract such dependencies through traditional methods such as granger causality or clustering. in this work, we present a novel deep learning model that uses multiple layers of customized gated recurrent units (grus) for discovering both time lagged behaviors as well as inter-timeseries dependencies in the form of directed weighted graphs. we introduce a key component of dual-purpose recurrent neural network that decodes information in the temporal domain to discover lagged dependencies within each time series, and encodes them into a set of vectors which, collected from all component time series, form the informative inputs to discover inter-dependencies. though the discovery of two types of dependencies are separated at different hierarchical levels, they are tightly connected and jointly trained in an end-to-end manner. with this joint training, learning of one type of dependency immediately impacts the learning of the other one, leading to overall accurate dependencies discovery. we empirically test our model on synthetic time series data in which the exact form of (non-linear) dependencies is known. we also evaluate its performance on two real-world applications, (i) performance monitoring data from a commercial cloud provider, which exhibit highly dynamic, non-linear, and volatile behavior and, (ii) sensor data from a manufacturing plant. we further show how our approach is able to capture these dependency behaviors via intuitive and interpretable dependency graphs and use them to generate highly accurate forecasts. |
semi-supervised dual graph regularized dictionary learning | in this paper, we propose a semi-supervised dictionary learning method that uses both the information in labelled and unlabelled data and jointly trains a linear classifier embedded on the sparse codes. the manifold structure of the data in the sparse code space is preserved using the same approach as the locally linear embedding method (lle). this enables one to enforce the predictive power of the unlabelled data sparse codes. we show that our approach provides significant improvements over other methods. the results can be further improved by training a simple nonlinear classifier as svm on the sparse codes. |
trade selection with supervised learning and oca | in recent years, state-of-the-art methods for supervised learning have exploited increasingly gradient boosting techniques, with mainstream efficient implementations such as xgboost or lightgbm. one of the key points in generating proficient methods is feature selection (fs). it consists in selecting the right valuable effective features. when facing hundreds of these features, it becomes critical to select best features. while filter and wrappers methods have come to some maturity, embedded methods are truly necessary to find the best features set as they are hybrid methods combining features filtering and wrapping. in this work, we tackle the problem of finding through machine learning best a priori trades from an algorithmic strategy. we derive this new method using coordinate ascent optimization and using block variables. we compare our method to recursive feature elimination (rfe) and binary coordinate ascent (bca). we show on a real life example the capacity of this method to select good trades a priori. not only this method outperforms the initial trading strategy as it avoids taking loosing trades, it also surpasses other method, having the smallest feature set and the highest score at the same time. the interest of this method goes beyond this simple trade classification problem as it is a very general method to determine the optimal feature set using some information about features relationship as well as using coordinate ascent optimization. |
the impact of quantity of training data on recognition of eating gestures | this paper considers the problem of recognizing eating gestures by tracking wrist motion. eating gestures can have large variability in motion depending on the subject, utensil, and type of food or beverage being consumed. previous works have shown viable proofs-of-concept of recognizing eating gestures in laboratory settings with small numbers of subjects and food types, but it is unclear how well these methods would work if tested on a larger population in natural settings. as more subjects, locations and foods are tested, a larger amount of motion variability could cause a decrease in recognition accuracy. to explore this issue, this paper describes the collection and annotation of 51,614 eating gestures taken by 269 subjects eating a meal in a cafeteria. experiments are described that explore the complexity of hidden markov models (hmms) and the amount of training data needed to adequately capture the motion variability across this large data set. results found that hmms needed a complexity of 13 states and 5 gaussians to reach a plateau in accuracy, signifying that a minimum of 65 samples per gesture type are needed. results also found that 500 training samples per gesture type were needed to identify the point of diminishing returns in recognition accuracy. overall, the findings provide evidence that the size a data set typically used to demonstrate a laboratory proofs-of-concept may not be sufficiently large enough to capture all the motion variability that could be expected in transitioning to deployment with a larger population. our data set, which is 1-2 orders of magnitude larger than all data sets tested in previous works, is being made publicly available. |
bounding the error from reference set kernel maximum mean discrepancy | in this paper, we bound the error induced by using a weighted skeletonization of two data sets for computing a two sample test with kernel maximum mean discrepancy. the error is quantified in terms of the speed in which heat diffuses from those points to the rest of the data, as well as how at the weights on the reference points are, and gives a non-asymptotic, non-probabilistic bound. the result ties into the problem of the eigenvector triple product, which appears in a number of important problems. the error bound also suggests an optimization scheme for choosing the best set of reference points and weights. the method is tested on a several two sample test examples. |
dcase 2018 challenge: solution for task 5 | to address task 5 in the detection and classification of acoustic scenes and events (dcase) 2018 challenge, in this paper, we propose an ensemble learning system. the proposed system consists of three different models, based on convolutional neural network and long short memory recurrent neural network. with extracted features such as spectrogram and mel-frequency cepstrum coefficients from different channels, the proposed system can classify different domestic activities effectively. experimental results obtained from the provided development dataset show that good performance with f1-score of 92.19% can be achieved. compared with the baseline system, our proposed system significantly improves the performance of f1-score by 7.69%. |
reproduction report on "learn to pay attention" | we have successfully implemented the "learn to pay attention" model of attention mechanism in convolutional neural networks, and have replicated the results of the original paper in the categories of image classification and fine-grained recognition. |
contrastive training for models of information cascades | this paper proposes a model of information cascades as directed spanning trees (dsts) over observed documents. in addition, we propose a contrastive training procedure that exploits partial temporal ordering of node infections in lieu of labeled training links. this combination of model and unsupervised training makes it possible to improve on models that use infection times alone and to exploit arbitrary features of the nodes and of the text content of messages in information cascades. with only basic node and time lag features similar to previous models, the dst model achieves performance with unsupervised training comparable to strong baselines on a blog network inference task. unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model. |
learning representations of molecules and materials with atomistic neural networks | deep learning has been shown to learn efficient representations for structured data such as image, text or audio. in this chapter, we present neural network architectures that are able to learn efficient representations of molecules and materials. in particular, the continuous-filter convolutional network schnet accurately predicts chemical properties across compositional and configurational space on a variety of datasets. beyond that, we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition. |
deep neural networks meet csi-based authentication | the first step of a secure communication is authenticating legible users and detecting the malicious ones. in the last recent years, some promising schemes proposed using wireless medium network's features, in particular, channel state information (csi) as a means for authentication. these schemes mainly compare user's previous csi with the new received csi to determine if the user is in fact what it is claiming to be. despite high accuracy, these approaches lack the stability in authentication when the users rotate in their positions. this is due to a significant change in csi when a user rotates which mislead the authenticator when it compares the new csi with the previous ones. our approach presents a way of extracting features from raw csi measurements which are stable towards rotation. we extract these features by the means of a deep neural network. we also present a scenario in which users can be {efficiently} authenticated while they are at certain locations in an environment (even if they rotate); and, they will be rejected if they change their location. also, experimental results are presented to show the performance of the proposed scheme. |
context is key: new approaches to neural coherence modeling | we formulate coherence modeling as a regression task and propose two novel methods to combine techniques from our setup with pairwise approaches. the first of our methods is a model that we call "first-next," which operates similarly to selection sorting but conditions decision-making on information about already-sorted sentences. the second consists of a technique for adding context to regression-based models by concatenating sentence-level representations with an encoding of its corresponding out-of-order paragraph. this latter model achieves kendall-tau distance and positional accuracy scores that match or exceed the current state-of-the-art on these metrics. our results suggest that many of the gains that come from more complex, machine-translation inspired approaches can be achieved with simpler, more efficient models. |
generative adversarial networks for recovering missing spectral information | ultra-wideband (uwb) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability. however, this spectrum is also shared by many others communication systems, which causes missing information in the frequency bands. to recover this missing spectral information, we propose a generative adversarial network, called sargan, that learns the relationship between original and missing band signals by observing these training pairs in a clever way. initial results shows that this approach is promising in tackling this challenging missing band problem. |
gradient descent happens in a tiny subspace | we show that in a variety of large-scale deep learning scenarios the gradient dynamically converges to a very small subspace after a short period of training. the subspace is spanned by a few top eigenvectors of the hessian (equal to the number of classes in the dataset), and is mostly preserved over long periods of training. a simple argument then suggests that gradient descent may happen mostly in this subspace. we give an example of this effect in a solvable model of classification, and we comment on possible implications for optimization and learning. |
modeling longitudinal data on riemannian manifolds | when considering functional principal component analysis for sparsely observed longitudinal data that take values on a nonlinear manifold, a major challenge is how to handle the sparse and irregular observations that are commonly encountered in longitudinal studies. addressing this challenge, we provide theory and implementations for a manifold version of the principal analysis by conditional expectation (pace) procedure that produces representations intrinsic to the manifold, extending a well-established version of functional principal component analysis targeting sparsely sampled longitudinal data in linear spaces. key steps are local linear smoothing methods for the estimation of a fr\'echet mean curve, mapping the observed manifold-valued longitudinal data to tangent spaces around the estimated mean curve, and applying smoothing methods to obtain the covariance structure of the mapped data. dimension reduction is achieved via representations based on the first few leading principal components. a finitely truncated representation of the original manifold-valued data is then obtained by mapping these tangent space representations to the manifold. we show that the proposed estimates of mean curve and covariance structure achieve state-of-the-art convergence rates. for longitudinal emotional well-being data for unemployed workers as an example of time-dynamic compositional data that are located on a sphere, we demonstrate that our methods lead to interpretable eigenfunctions and principal component scores. in a second example, we analyze the body shapes of wallabies by mapping the relative size of their body parts onto a spherical pre-shape space. compared to standard functional principal component analysis, which is based on euclidean geometry, the proposed approach leads to improved trajectory recovery for sparsely sampled data on nonlinear manifolds. |
bridging the generalization gap: training robust models on confounded biological data | statistical learning on biological data can be challenging due to confounding variables in sample collection and processing. confounders can cause models to generalize poorly and result in inaccurate prediction performance metrics if models are not validated thoroughly. in this paper, we propose methods to control for confounding factors and further improve prediction performance. we introduce orthonormal basis construction in confounding factor normalization (onion) to remove confounding covariates and use the domain-adversarial neural network (dann) to penalize models for encoding confounder information. we apply the proposed methods to simulated and empirical patient data and show significant improvements in generalization. |