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职业迁徙
个人简介
My previous research was focused on the application of information theory to optimize resource allocation for data throughput and network reliability in advanced communication systems.
Optimal Model Refinement is my current research interest, centered around leveraging mathematical optimization to enhance the interpretability and efficiency of machine learning models. I explore strategies to streamline complex models without performance loss, as well as to unravel the intricate mechanisms of decision-making models. Central to this pursuit is understanding the synergy between model simplification and explainability: Reducing a model's complexity aids in elucidating its functions, and concurrently, and explainability drives the efficient compression of the model for learning.
Optimal Model Refinement is my current research interest, centered around leveraging mathematical optimization to enhance the interpretability and efficiency of machine learning models. I explore strategies to streamline complex models without performance loss, as well as to unravel the intricate mechanisms of decision-making models. Central to this pursuit is understanding the synergy between model simplification and explainability: Reducing a model's complexity aids in elucidating its functions, and concurrently, and explainability drives the efficient compression of the model for learning.
研究兴趣
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CoRR (2024)
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IEEE Journal on Selected Areas in Communicationsno. 99 (2024): 1-1
IEEE networking lettersno. 4 (2023): 199-203
ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (2023): 5104-5110
International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)pp.265-269, (2021)
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