| Abstract |
Deep learning, represented by deep artificial neural networks, has been dominating numerous important research fields in the past decade. Although the invention of the neural network was to mimic a human's brain, the current development of deep learning is not primarily driven by the increasingly growing understanding to the brain. Brain is the most intelligent system we have ever known so far, although the brain remains vastly undiscovered, it is clear that the existing deep learning still goes far behind human brain in many important aspects such as efficiency, interpretability, memory, etc. Given the incredible capability of the human brain, we argue that neuroscience can always offer support for deep learning as a think tank and a validation means. In this talk, we discuss drawing the mechanism of genome bottleneck into deep learning, with an emphasis on solving problems in model compression, which is to facilitate the deployment of large models. |
| About the speaker |
Dr. Fenglei Fan is currently an Assistant Professor with Department of Data Science, City University of Hong Kong. His primary research interests lie in NeuroAI and its applications in model compression and medical imaging. He was the recipient of the IBM AI Horizon Scholarship. His PhD dissertation was also selected as the recipient of the 2021 International Neural Network Society Doctoral Dissertation Award. His two primarily-authored papers were selected as one of few 2024 CVPR Best Paper Award Candidates (26 out of 1W+submissions) and won the IEEE Nuclear and Plasma Society IEEE TRPMS Best Paper Award, respectively. He organized special issue in journals like IEEE TRPMS, presented three tutorials in AAAI2023 and WWW2025, and served as (senior) program committee members in AAAI and IJCAI.
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