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Collections including paper arxiv:1701.06538

MoEs papers reading list

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 5
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 4
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

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

Must Reads On Transformers and Diffusers

Explore the cutting-edge of AI with our curated list of must reads on Transformers & Diffusers, driving innovation in generative-AI and beyond.

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 5
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 50
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Paper • 2005.11401 • Published May 22, 2020 • 11
Language Model Evaluation Beyond Perplexity

Paper • 2106.00085 • Published May 31, 2021

Papers - MoE - Scaling

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 5
ST-MoE: Designing Stable and Transferable Sparse Expert Models

Paper • 2202.08906 • Published Feb 17, 2022 • 2
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

Paper • 2403.07816 • Published Mar 12, 2024 • 39

Papers - MoE - Training

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Paper • 2308.10110 • Published Aug 19, 2023 • 2
Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2
ConstitutionalExperts: Training a Mixture of Principle-based Prompts

Paper • 2403.04894 • Published Mar 7, 2024 • 2
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models

Paper • 2403.03432 • Published Mar 6, 2024 • 1

Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts

Paper • 2009.10622 • Published Sep 22, 2020 • 1
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29, 2024 • 49
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Paper • 2401.04081 • Published Jan 8, 2024 • 70
MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

Paper • 2401.14361 • Published Jan 25, 2024 • 2

Mixture of Experts Papers

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29, 2024 • 49
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11, 2024 • 44
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention

Paper • 2312.07987 • Published Dec 13, 2023 • 41
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Paper • 2101.03961 • Published Jan 11, 2021 • 14

3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features

Paper • 2311.04391 • Published Nov 7, 2023 • 9
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 5
mattshumer/Reflection-Llama-3.1-70B

Text Generation • Updated Sep 24, 2024 • 641 • 1.71k

mixture-of-experts

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 5
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 4
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

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

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