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Collections including paper arxiv:2101.03961

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 17
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16 • 74
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

a growing collection of arXiv papers read while learning ML 📜

SMOTE: Synthetic Minority Over-sampling Technique

Paper • 1106.1813 • Published Jun 9, 2011 • 1
Scikit-learn: Machine Learning in Python

Paper • 1201.0490 • Published Jan 2, 2012 • 1
Identity Mappings in Deep Residual Networks

Paper • 1603.05027 • Published Mar 16, 2016 • 2
Deep Residual Learning for Image Recognition

Paper • 1512.03385 • Published Dec 10, 2015 • 5

Mixture of Experts Papers

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

Paper • 2401.15947 • Published Jan 29 • 46
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11 • 35
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention

Paper • 2312.07987 • Published Dec 13, 2023 • 39
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Paper • 2101.03961 • Published Jan 11, 2021 • 13

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Paper • 2101.03961 • Published Jan 11, 2021 • 13

mixture-of-experts

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

Paper • 1701.06538 • Published Jan 23, 2017 • 4
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 • 3
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

MoEs papers reading list

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

Paper • 1701.06538 • Published Jan 23, 2017 • 4
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 • 3
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 34
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 11
RoBERTa: A Robustly Optimized BERT Pretraining Approach

Paper • 1907.11692 • Published Jul 26, 2019 • 7
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Paper • 1910.01108 • Published Oct 2, 2019 • 10

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

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 566
Mixtral of Experts

Paper • 2401.04088 • Published Jan 8 • 152
Mistral 7B

Paper • 2310.06825 • Published Oct 10, 2023 • 43
Don't Make Your LLM an Evaluation Benchmark Cheater

Paper • 2311.01964 • Published Nov 3, 2023 • 1

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