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ArXiv ML Papers
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ArXiv ML Papers
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ArXiv ML Papers
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ArXiv ML Papers
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11.415018
all-MiniLM-L6-v2
0.357895
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0.179466
0.564226
ArXiv ML Papers
BERTopic
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30
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ArXiv ML Papers
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15.499882
all-MiniLM-L6-v2
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0.170969
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ArXiv ML Papers
BERTopic
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40
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26.208948
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13.015473
all-MiniLM-L6-v2
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ArXiv ML Papers
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31.63049
all-MiniLM-L6-v2
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0.164638
0.597184
ArXiv ML Papers
BERTopic
43
50
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12.399162
all-MiniLM-L6-v2
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ArXiv ML Papers
BERTopic
45
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498.173665
all-MiniLM-L6-v2
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0.17295
0.592389
ArXiv ML Papers
BERTopic
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529.846204
all-MiniLM-L6-v2
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ArXiv ML Papers
NMF
43
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1.813817
all-MiniLM-L6-v2
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0.195331
0.467242
ArXiv ML Papers
NMF
44
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all-MiniLM-L6-v2
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0.195331
0.464778
ArXiv ML Papers
NMF
45
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2.06287
all-MiniLM-L6-v2
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0.195331
0.469553
ArXiv ML Papers
NMF
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1.930904
all-MiniLM-L6-v2
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0.195331
0.47231
ArXiv ML Papers
NMF
43
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2.422656
all-MiniLM-L6-v2
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0.561412
ArXiv ML Papers
NMF
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NMF
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ArXiv ML Papers
NMF
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ArXiv ML Papers
NMF
43
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6.24702
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0.67742
ArXiv ML Papers
NMF
44
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6.245105
all-MiniLM-L6-v2
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0.150291
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ArXiv ML Papers
NMF
45
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6.301346
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6.587935
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ArXiv ML Papers
LDA
43
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10.928315
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ArXiv ML Papers
LDA
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ArXiv ML Papers
LDA
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19.839619
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ArXiv ML Papers
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10.933623
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ArXiv ML Papers
LDA
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12.112303
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ArXiv ML Papers
LDA
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21.244746
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ArXiv ML Papers
LDA
45
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20.694685
all-MiniLM-L6-v2
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0.557579
ArXiv ML Papers
LDA
46
20
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11.831478
all-MiniLM-L6-v2
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ArXiv ML Papers
LDA
43
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13.241964
all-MiniLM-L6-v2
0.4
-0.039303
0.200602
0.60234
ArXiv ML Papers
LDA
44
30
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13.062546
all-MiniLM-L6-v2
0.416667
-0.046981
0.187724
0.604609
ArXiv ML Papers
LDA
45
30
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12.018622
all-MiniLM-L6-v2
0.443333
-0.071942
0.169726
0.634942
ArXiv ML Papers
LDA
46
30
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12.875089
all-MiniLM-L6-v2
0.336667
-0.098042
0.191397
0.648427
ArXiv ML Papers
LDA
43
40
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12.933721
all-MiniLM-L6-v2
0.3475
-0.043755
0.195528
0.61362
ArXiv ML Papers
LDA
44
40
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13.056187
all-MiniLM-L6-v2
0.3425
-0.037738
0.207822
0.592155
ArXiv ML Papers
LDA
45
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13.227212
all-MiniLM-L6-v2
0.4
-0.058412
0.186957
0.626419
ArXiv ML Papers
LDA
46
40
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13.765348
all-MiniLM-L6-v2
0.3825
-0.072976
0.192218
0.644522
ArXiv ML Papers
LDA
43
50
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23.874444
all-MiniLM-L6-v2
0.364
-0.051004
0.189744
0.61902
ArXiv ML Papers
LDA
44
50
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14.476796
all-MiniLM-L6-v2
0.356
-0.035463
0.186418
0.596312
ArXiv ML Papers
LDA
45
50
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14.255966
all-MiniLM-L6-v2
0.39
-0.061498
0.18372
0.637337
ArXiv ML Papers
LDA
46
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14.030783
all-MiniLM-L6-v2
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Top2Vec
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656.29102
all-MiniLM-L6-v2
0.54
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0.14962
0.846283
ArXiv ML Papers
Top2Vec
44
10
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673.252285
all-MiniLM-L6-v2
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ArXiv ML Papers
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45
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584.320705
all-MiniLM-L6-v2
0.56
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0.174848
0.846271
ArXiv ML Papers
Top2Vec
46
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634.524863
all-MiniLM-L6-v2
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0.153078
0.853081
ArXiv ML Papers
Top2Vec
43
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608.984458
all-MiniLM-L6-v2
0.45
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0.173274
0.85077
ArXiv ML Papers
Top2Vec
44
20
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535.264348
all-MiniLM-L6-v2
0.47
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0.174505
0.844253
ArXiv ML Papers
Top2Vec
45
20
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588.672674
all-MiniLM-L6-v2
0.455
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0.178586
0.840558
ArXiv ML Papers
Top2Vec
46
20
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631.574545
all-MiniLM-L6-v2
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ArXiv ML Papers
Top2Vec
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577.164902
all-MiniLM-L6-v2
0.452381
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0.17378
0.84458
ArXiv ML Papers
Top2Vec
44
30
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601.105897
all-MiniLM-L6-v2
0.480952
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0.172465
0.844825
ArXiv ML Papers
Top2Vec
45
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597.81598
all-MiniLM-L6-v2
0.442857
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0.175005
0.843511
ArXiv ML Papers
Top2Vec
46
30
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498.065006
all-MiniLM-L6-v2
0.452381
-0.262968
0.171184
0.850088
ArXiv ML Papers
Top2Vec
43
40
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640.464483
all-MiniLM-L6-v2
0.452381
-0.264192
0.17378
0.845774
ArXiv ML Papers
Top2Vec
44
40
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569.045129
all-MiniLM-L6-v2
0.480952
-0.2564
0.172465
0.846093
ArXiv ML Papers
Top2Vec
45
40
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569.015667
all-MiniLM-L6-v2
0.442857
-0.252986
0.175005
0.842255
ArXiv ML Papers
Top2Vec
46
40
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584.353214
all-MiniLM-L6-v2
0.452381
-0.262968
0.171184
0.844559
ArXiv ML Papers
Top2Vec
43
50
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498.800022
all-MiniLM-L6-v2
0.452381
-0.264192
0.17378
0.850448
ArXiv ML Papers
Top2Vec
44
50
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530.921954
all-MiniLM-L6-v2
0.480952
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0.172465
0.845788
ArXiv ML Papers
Top2Vec
45
50
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651.461712
all-MiniLM-L6-v2
0.442857
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0.175005
0.843242
ArXiv ML Papers
Top2Vec
46
50
[ [ "learning", "ai", "planning", "reinforcement", "bandit", "learnt", "learns", "bandits", "learnable", "softmax" ], [ "adversarially", "distributed", "softmax", "federated", "learning", "learns", "learnt", "supervised", "rnns", "rnn" ], [ "adversary", "adversarial", "adversarially", "cnns", "adversaries", "cnn", "classifiers", "attacks", "softmax", "attacker" ], [ "regularization", "adversarially", "adversarial", "privacy", "private", "randomized", "adversary", "normalization", "regularized", "softmax" ], [ "fairness", "adversarially", "bias", "biases", "classifiers", "discrimination", "adversarial", "discriminate", "supervised", "classifier" ], [ "softmax", "autoencoder", "lstm", "supervised", "rnns", "embeddings", "learns", "corpus", "autoencoders", "cnn" ], [ "learns", "rnns", "neural", "backpropagation", "learning", "softmax", "autoencoders", "models", "rnn", "modeling" ], [ "forecasts", "forecasting", "forecast", "prediction", "predict", "predicting", "predicts", "softmax", "classifiers", "rnns" ], [ "softmax", "networks", "neural", "backpropagation", "cnns", "imagenet", "regularization", "cnn", "neuron", "rnns" ], [ "classify", "classifying", "eeg", "supervised", "classifiers", "classifier", "classification", "recognition", "lstm", "svm" ], [ "networks", "graphs", "nodes", "graph", "cnn", "softmax", "cnns", "supervised", "vertex", "embeddings" ], [ "dataset", "classifying", "classifiers", "predicting", "datasets", "softmax", "supervised", "classification", "classify", "rnns" ], [ "classifiers", "ensemble", "softmax", "boosting", "supervised", "classification", "classifying", "classify", "classifier", "ensembles" ], [ "imagenet", "cnns", "cnn", "supervised", "softmax", "neural", "recognition", "autoencoders", "segmentation", "recognizing" ], [ "regularization", "classifiers", "metrics", "classifier", "classifying", "classification", "softmax", "supervised", "similarity", "metric" ], [ "sparse", "softmax", "clustering", "minimization", "optimizer", "lasso", "algorithms", "regularized", "regularization", "supervised" ], [ "gan", "autoencoders", "adversarially", "adversarial", "autoencoder", "generative", "imagenet", "gans", "cnns", "cnn" ], [ "cnns", "rnns", "cnn", "imagenet", "autoencoders", "bottleneck", "benchmarks", "networks", "softmax", "neural" ], [ "imagenet", "recognizing", "recognition", "convolutions", "cnns", "cnn", "supervised", "autoencoders", "convolutional", "softmax" ], [ "optimization", "minimization", "optimizer", "optimizing", "optimize", "minimize", "regularization", "lasso", "optimized", "optimizes" ], [ "autoencoders", "learns", "rnns", "generative", "softmax", "models", "supervised", "backpropagation", "priors", "autoencoder" ] ]
640.067949
all-MiniLM-L6-v2
0.452381
-0.262968
0.171184
0.850129
ArXiv ML Papers
GMM
43
10
[ [ "kernel", "clustering", "matrix", "rank", "series", "time", "regression", "proposed", "tensor", "algorithm" ], [ "gnns", "link", "node", "embedding", "graphs", "graph", "nodes", "structure", "networks", "social" ], [ "reward", "rl", "reinforcement", "policy", "agent", "agents", "policies", "games", "action", "control" ], [ "speech", "text", "language", "languages", "attention", "word", "words", "sequence", "task", "natural" ], [ "images", "gan", "gans", "image", "generative", "latent", "generation", "generator", "variational", "vae" ], [ "convergence", "bounds", "gradient", "stochastic", "convex", "optimization", "algorithm", "bound", "complexity", "sample" ], [ "fairness", "ml", "label", "classifier", "machine", "classification", "decision", "prediction", "their", "research" ], [ "networks", "deep", "memory", "architectures", "layer", "systems", "quantum", "neural", "network", "time" ], [ "segmentation", "images", "object", "cnn", "convolutional", "image", "deep", "detection", "video", "visual" ], [ "perturbations", "against", "robustness", "robust", "attack", "attacks", "defense", "adversarial", "privacy", "examples" ] ]
2.576472
all-MiniLM-L6-v2
0.94
0.053246
0.1459
0.813589
ArXiv ML Papers
GMM
44
10
[ [ "speech", "recognition", "speaker", "acoustic", "asr", "audio", "music", "signal", "end", "quality" ], [ "posterior", "variational", "inference", "estimation", "bayesian", "distribution", "latent", "gaussian", "series", "processes" ], [ "classifier", "classification", "machine", "label", "prediction", "metric", "accuracy", "datasets", "ml", "features" ], [ "reinforcement", "reward", "agent", "agents", "rl", "action", "control", "policy", "games", "policies" ], [ "attack", "defense", "perturbations", "privacy", "attacks", "adversarial", "robustness", "against", "robust", "examples" ], [ "graphs", "node", "graph", "nodes", "embedding", "link", "structure", "gnns", "embeddings", "representation" ], [ "algorithm", "convex", "convergence", "regret", "bound", "stochastic", "bounds", "algorithms", "optimization", "gradient" ], [ "memory", "layer", "network", "networks", "neural", "accuracy", "architectures", "deep", "architecture", "input" ], [ "image", "segmentation", "object", "visual", "images", "gan", "convolutional", "dataset", "deep", "detection" ], [ "sequence", "sentences", "word", "natural", "language", "text", "task", "words", "attention", "semantic" ] ]
2.836519
all-MiniLM-L6-v2
0.98
0.06128
0.149774
0.825734
ArXiv ML Papers
GMM
45
10
[ [ "detection", "segmentation", "visual", "gan", "image", "images", "object", "convolutional", "supervised", "medical" ], [ "examples", "against", "adversarial", "defense", "perturbations", "attack", "attacks", "privacy", "robust", "robustness" ], [ "agent", "reinforcement", "reward", "rl", "policy", "agents", "regret", "action", "policies", "games" ], [ "text", "speech", "attention", "audio", "language", "languages", "speaker", "word", "translation", "recurrent" ], [ "quantum", "posterior", "parameters", "variational", "bayesian", "physics", "uncertainty", "dynamics", "inference", "equations" ], [ "series", "classifier", "prediction", "classification", "machine", "time", "accuracy", "selection", "label", "regression" ], [ "convergence", "algorithm", "convex", "matrix", "optimization", "rank", "algorithms", "linear", "problems", "gradient" ], [ "neural", "layer", "network", "networks", "deep", "architectures", "accuracy", "memory", "hardware", "layers" ], [ "user", "items", "language", "recommendation", "item", "knowledge", "label", "recommender", "topic", "metric" ], [ "nodes", "graph", "graphs", "node", "link", "gnns", "embedding", "structure", "embeddings", "representation" ] ]
2.766196
all-MiniLM-L6-v2
0.97
0.024243
0.165378
0.838997
ArXiv ML Papers
GMM
46
10
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2.882266
all-MiniLM-L6-v2
0.98
0.054135
0.155939
0.826401
ArXiv ML Papers
GMM
43
20
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4.033339
all-MiniLM-L6-v2
0.9
0.016575
0.153815
0.857305
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