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

 This Week
 Next Week
 Online Talks >
 ...by date

 This Week
 Next Week
 All Talks

Speakers: Please contact us about file upload for your slides.

Time Speaker Title
11/14, 10:00am All Participants Welcome
11/14, 10:30am Ila Fiete
New models of content-addressable memory from biology to transformers[Video][CC]
11/14, 11:15am Sho Yaida
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
Understanding the Universality Phenomenon in High-Dimensional Estimation and Learning: Some Recent Progress[Video][CC]
11/17, 11:00am Sho Yaida
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
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
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
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
Inductive biases facilitating relational learning and abstraction in neural networks
11/30, 10:30am David Klindt
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
email: contact | printer friendly