Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts
Paper
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2009.10622
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Published
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1
Papers - MoE
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MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
Paper • 2401.15947 • Published • 49 -
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
Paper • 2401.04081 • Published • 70 -
MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving
Paper • 2401.14361 • Published • 2 -
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Paper • 2308.12066 • Published • 4 -
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models
Paper • 2308.14352 • Published -
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Paper • 2308.06093 • Published • 2 -
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense
Paper • 2402.18787 • Published • 2 -
CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition
Paper • 2402.02526 • Published • 3 -
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Paper • 2006.16668 • Published • 3 -
Scaling Vision with Sparse Mixture of Experts
Paper • 2106.05974 • Published • 3 -
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Paper • 2211.15841 • Published • 7 -
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Paper • 1701.06538 • Published • 5 -
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
Paper • 2402.01739 • Published • 26 -
ST-MoE: Designing Stable and Transferable Sparse Expert Models
Paper • 2202.08906 • Published • 2 -
LocMoE: A Low-overhead MoE for Large Language Model Training
Paper • 2401.13920 • Published • 2 -
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
Paper • 2201.05596 • Published • 2 -
Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism
Paper • 2304.11414 • Published • 2 -
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
Paper • 2401.06066 • Published • 43 -
AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction
Paper • 2402.08698 • Published • 2 -
Routers in Vision Mixture of Experts: An Empirical Study
Paper • 2401.15969 • Published • 2 -
BASE Layers: Simplifying Training of Large, Sparse Models
Paper • 2103.16716 • Published • 3 -
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Paper • 2106.03760 • Published • 3 -
Hash Layers For Large Sparse Models
Paper • 2106.04426 • Published • 2 -
Direct Neural Machine Translation with Task-level Mixture of Experts models
Paper • 2310.12236 • Published • 2 -
Adaptive Gating in Mixture-of-Experts based Language Models
Paper • 2310.07188 • Published • 2 -
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
Paper • 2310.01334 • Published • 3 -
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
Paper • 2309.04354 • Published • 13 -
Towards More Effective and Economic Sparsely-Activated Model
Paper • 2110.07431 • Published • 2 -
Taming Sparsely Activated Transformer with Stochastic Experts
Paper • 2110.04260 • Published • 2 -
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference
Paper • 2110.03742 • Published • 3 -
FedJETs: Efficient Just-In-Time Personalization with Federated Mixture of Experts
Paper • 2306.08586 • Published • 1 -
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
Paper • 2306.04845 • Published • 4 -
Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture
Paper • 2306.11982 • Published • 2 -
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Paper • 2403.07508 • Published • 74 -
Unified Scaling Laws for Routed Language Models
Paper • 2202.01169 • Published • 2 -
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 26 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 104 -
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints
Paper • 2212.05055 • Published • 5 -
JetMoE: Reaching Llama2 Performance with 0.1M Dollars
Paper • 2404.07413 • Published • 36 -
Fast Feedforward Networks
Paper • 2308.14711 • Published • 3 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 12 -
Qwen2 Technical Report
Paper • 2407.10671 • Published • 160 -
Mixture of A Million Experts
Paper • 2407.04153 • Published • 5 -
Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
Paper • 2408.12570 • Published • 30 -
Qwen2.5 Technical Report
Paper • 2412.15115 • Published • 325