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Rethinking Learning Rate Tuning in the Era of Large Language Models

Published in 2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI), 2023

This paper explores the challenges of learning rate tuning for Large Language Models (LLMs) and introduces LRBench++ for benchmarking.

Recommended citation: Jin, H., Wei, W., Wang, X., Zhang, W., & Wu, Y. (2023). "Rethinking Learning Rate Tuning in the Era of Large Language Models." 2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI), 112-121.
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DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

Published in arXiv Preprint, 2024

This paper proposes DA-MoE, a novel dynamic router mechanism for Mixture-of-Experts (MoE) models, enabling efficient expert allocation based on token importance.

Recommended citation: Aghdam, M. A., Jin, H., & Wu, Y. (2024). "DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models." arXiv Preprint. arXiv:2409.06669.
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CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration

Published in arXiv Preprint, 2024

This paper proposes CE-CoLLM, a novel cloud-edge collaboration framework for efficient and adaptive inference of Large Language Models (LLMs).

Recommended citation: Jin, H., & Wu, Y. (2024). "CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration." arXiv Preprint. arXiv:2411.02829.
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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