Adaptive sequential Monte Carlo by means of mixture of experts
Paper
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1108.2836
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Published
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2
Papers - MoE - Research
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Convergence Rates for Mixture-of-Experts
Paper • 1110.2058 • Published • 2 -
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
Paper • 2310.12008 • Published • 2 -
Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts
Paper • 2308.11793 • Published • 2 -
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Paper • 2308.10110 • Published • 2 -
HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion
Paper • 2308.06512 • Published • 2 -
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Paper • 2308.06093 • Published • 2 -
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models
Paper • 2403.03432 • Published • 1 -
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense
Paper • 2402.18787 • Published • 2 -
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
Paper • 2402.14800 • Published • 3 -
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
Paper • 2402.12550 • Published • 2 -
Turn Waste into Worth: Rectifying Top-k Router of MoE
Paper • 2402.12399 • Published • 2 -
Buffer Overflow in Mixture of Experts
Paper • 2402.05526 • Published • 8 -
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Paper • 2211.15841 • Published • 7 -
A Machine Learning Perspective on Predictive Coding with PAQ
Paper • 1108.3298 • Published • 2 -
DEMix Layers: Disentangling Domains for Modular Language Modeling
Paper • 2108.05036 • Published • 3 -
Sparse Backpropagation for MoE Training
Paper • 2310.00811 • Published • 2 -
A Review of Sparse Expert Models in Deep Learning
Paper • 2209.01667 • 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 -
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 -
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints
Paper • 2212.05055 • Published • 5