KITP Program: Deep Learning from the Perspective of Physics and Neuroscience

KITP Program: Deep Learning from the Perspective of Physics and Neuroscience
(Nov 13 - Dec 22, 2023)
Coordinators: Yasaman Bahri, Cengiz Pehlevan and Haim Sompolinsky


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Time	Speaker	Title
11/14, 10:00am	All Participants	Welcome
11/14, 10:30am	Ila Fiete MIT	New models of content-addressable memory from biology to transformers[Video][CC]
11/14, 11:15am	Sho Yaida META	Effective Theory of Transformers[Slides][Video][CC]
11/16, 9:45am	Guillaume Lajoie Univ de Montreal - Mila, Quebec AI Institute	Rich and Lazy neurons: network connectivity structure and the double implications of feature learning for generalization[Video][CC]
11/16, 10:30am	Dmitry Krotov MIT-IBM Watson AI Lab & IBM Research	Dense Associative Memory for novel Transformer architectures[Video][CC]
11/16, 11:15am	Yue Lu Harvard	Understanding the Universality Phenomenon in High-Dimensional Estimation and Learning: Some Recent Progress[Video][CC]
11/17, 11:00am	Sho Yaida META	Tutorial on Transformers with Indices[Slides][Video][CC]
11/20, 12:15pm	Haim Sompolinsky Hebrew Univ.	Statistical Mechanics of Deep Learning [Video] KITP Blackboard Lunch
11/21, 9:45am	Tankut Can Institute for Advanced Study	LLM-assisted study of human memory for meaningful narratives[Video][CC]
11/21, 10:30am	Gautam Reddy Princeton	Data dependence and abrupt transitions during in-context learning[Video][CC]
11/21, 11:15am	Dan Lee Cornell Tech	Perceptrons Revisited[Video][CC]
11/22, 9:45am	Qianyi Li Harvard	Beyond the Kernel Regime: Analytical Approaches to Single and Sequential Task Learning[Video][CC]
11/22, 10:30am	Bruno Olshausen UC Berkeley	On incorporating mathematical and biological structure into neural network models[Video][CC]
11/22, 11:15am	Francesca Mignacco Princeton & CUNY	Statistical physical insights into the dynamics of learning algorithms[Video][CC]
11/22, 3:00pm	Jamie Simon UC Berkeley	Tutorial on "kernel/lazy" vs "rich/feature-learning" regimes in wide nets[Video]
11/28, 9:45am	Mikhail Belkin UCSD	Toward a practical theory of deep learning: feature learning in deep neural networks and backpropagation-free algorithms that learn features
11/28, 10:30am	Dmitri Chklovskii Flatiron Institute	Reimagining the neuron as a controller: A novel model for Neuroscience and AI
11/28, 11:15am	Mate Lengyel University of Cambridge	Continual learning the biological way a?" contextual inference is key
11/29, 11:00am	Cathy Chen (UC Berkeley) Ariel Goldstein (Hebrew University)	Discussion Session on NLP, LLMs and the human brain
11/30, 9:45am	Kamesh Krishnamurthy Princeton	Inductive biases facilitating relational learning and abstraction in neural networks
11/30, 10:30am	David Klindt Stanford	Identifying Interpretable Visual Features in Artificial and Biological Neural Systems
11/30, 11:15am	Michael Bonner Johns Hopkins University	A high-dimensional view of computational neuroscience

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