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Collections including paper arxiv:2306.04073

Papers - MoE - Router - Research

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts

Paper • 2306.04845 • Published Jun 8, 2023 • 4
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

Paper • 2306.04073 • Published Jun 7, 2023 • 2
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

Paper • 2403.07816 • Published Mar 12 • 39
Unified Scaling Laws for Routed Language Models

Paper • 2202.01169 • Published Feb 2, 2022 • 2

Papers - MoE - Image

Scaling Vision with Sparse Mixture of Experts

Paper • 2106.05974 • Published Jun 10, 2021 • 3
Routers in Vision Mixture of Experts: An Empirical Study

Paper • 2401.15969 • Published Jan 29 • 2
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts

Paper • 2206.02770 • Published Jun 6, 2022 • 3
Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2

Papers - MoE - Router

Turn Waste into Worth: Rectifying Top-k Router of MoE

Paper • 2402.12399 • Published Feb 17 • 2
CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition

Paper • 2402.02526 • Published Feb 4 • 3
Buffer Overflow in Mixture of Experts

Paper • 2402.05526 • Published Feb 8 • 8
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Paper • 2402.01739 • Published Jan 29 • 26

MOE papers to read

Copied from MoE using https://huggingface.co./spaces/librarian-bots/collection_cloner.

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

Woodpecker: Hallucination Correction for Multimodal Large Language Models

Paper • 2310.16045 • Published Oct 24, 2023 • 14
HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models

Paper • 2310.14566 • Published Oct 23, 2023 • 25
SILC: Improving Vision Language Pretraining with Self-Distillation

Paper • 2310.13355 • Published Oct 20, 2023 • 6
Conditional Diffusion Distillation

Paper • 2310.01407 • Published Oct 2, 2023 • 20

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