報告題目： Neuronal Diversity in Deep Learning
報告時間：2023年11月14日（周二）上午9:30-10:30
報告地點(diǎn)：多學(xué)科交叉創(chuàng)新研究大樓919報告廳
報告內(nèi)容：
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. Clearly, the characters of the current mainstream deep learning models are fundamentally different from the biological neural system. One remarkable distinction is that the deep learning models lack the neuronal diversity that is everywhere in the human brain. Different from artificial networks that are built on a single universal primitive neuron type, the human brain has numerous morphologically and functionally diverse neurons. The neuronal diversity is an enabling factor for all kinds of intelligent behaviors. In this talk, I will discuss what values can the neuronal diversity potentially add to the artificial neural network.
報告人簡介：
范鳳磊，男，博士，香港中文大學(xué)數(shù)學(xué)系研究助理教授，研究方向為深度學(xué)習(xí)理論，建模與應(yīng)用。本科畢業(yè)于哈爾濱工業(yè)大學(xué)，博士畢業(yè)于美國倫斯勒理工學(xué)院（Rensselaer Polytechnic Institute），團(tuán)隊導(dǎo)師為國際知名影像專家王革教授, 隨后在美國康乃爾大學(xué)完成為期一年的博士后研究。發(fā)表論文20余篇，主要研究成果發(fā)表于人工智能領(lǐng)域和圖像處理領(lǐng)域的旗艦雜志如JMLR, IEEE TNNLS, IEEE TMI, IEEE TCI, IEEE TAI, 代表成果為基于二階神經(jīng)元的深度學(xué)習(xí)體系和神經(jīng)網(wǎng)絡(luò)寬度深度對稱性。據(jù) Google Scholar 統(tǒng)計，成果被麥克阿瑟天才獎得主兼加州理工大學(xué)教授Colin Camerer、ACM 會士C.-C. Jay Kuo、美國工程院院士Charbel Farhat、IEEE會士Michael Unser等人引用800余次。在人工智能頂級會議AAAI2023組織tutorial，獲得廣泛關(guān)注和好評，并受邀擔(dān)任中科院二區(qū)雜志Frontiers in Human Neuroscience 特刊“ Brain Imaging, Stimulation, and Analysis”的客座編輯。攻讀博士學(xué)位期間獲得IBM AI Horizon Scholarship的資助（共計20w美金），并受邀前往麻省理工學(xué)院-IBM人工智能實驗室實習(xí)。博士論文被國際神經(jīng)網(wǎng)絡(luò)協(xié)會（INNS）授予2021年博士論文獎（每年僅授予一名）。
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