Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference | Paper Citation Record

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This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 6

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This paper cites A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Mixtral of Experts

Reference 9

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This paper cites Looking beyond the surface: A challenge set for reading comprehension over multiple sentences.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Looking beyond the surface: A challenge set for reading comprehension over multiple sentences

Reference 10

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This paper cites RACE: Large-scale ReAding Comprehension Dataset From Examinations.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference RACE: Large-scale ReAding Comprehension Dataset From Examinations

Reference 11

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This paper cites Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 15

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This paper cites Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts

Reference 16

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This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 17

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This paper cites JetMoE: Reaching Llama2 Performance with 0.1M Dollars.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference JetMoE: Reaching Llama2 Performance with 0.1M Dollars

Reference 19

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 21

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This paper cites doi: 10.18653/v1/W18-5446.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference doi: 10.18653/v1/W18-5446

Reference 23

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This paper cites A broad- coverage challenge corpus for sentence understanding through inference.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference A broad- coverage challenge corpus for sentence understanding through inference

Reference 24

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This paper cites MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition

Reference 25

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference HellaSwag: Can a Machine Really Finish Your Sentence?

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Reference 28

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Reference 29

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Reference 30

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Reference 31

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This paper cites Apart from these tasks, router-based pruning outperforms similarity-based pruning on11 benchmarks, while similarity-based pruning outperforms router-based pruning on7benchmarks.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Apart from these tasks, router-based pruning outperforms similarity-based pruning on11 benchmarks, while similarity-based pruning outperforms router-based pruning on7benchmarks

Reference 42

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Reference 48

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference MiniMax-01: Scaling Foundation Models with Lightning Attention

Reference 2012

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Reference 2013

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference GLUE: A multi-task benchmark and analysis plat- form for natural language understanding

Reference 2017

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference OLMoE: Open Mixture-of-Experts Language Models

Reference 2018

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Prost: Physical reasoning about objects through space and time

Reference 2019

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 2020

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference H., and Jiang, Y

Reference 2021

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 2022

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 2023

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Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference GPT-4 Technical Report

Reference 2024

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This paper cites Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy.

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy

Reference 2025

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Pith citing papers