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
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[Slides][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[Embargoed]
11/28, 10:30am Dmitri Chklovskii
Flatiron Institute
Reimagining the neuron as a controller: A novel model for Neuroscience and AI[Video][CC]
11/28, 11:15am Mate Lengyel
University of Cambridge
Continual learning - the biological way[Slides][Video][CC]
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 Brice Menard
Johns Hopkins University
Insights into how/what CNNs learn
11/30, 10:30am David Klindt
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems[Video]
11/30, 11:15am Michael Bonner
Johns Hopkins University
A high-dimensional view of computational neuroscience[Video][CC]
12/01, 10:00am Alexander Van Meegen
Blake Bordelon
Francesca Mignacco
Stefano Sarao Mannelli
Dynamical Mean Field Theory for Neural Networks
12/01, 1:00pm Mikhail Belkin
Backpropagation-free algorithms that learn features
12/05, 9:45am Andrew Saxe
The Neural Race Reduction: Feature learning dynamics in deep architectures[Video][CC]
12/05, 10:30am Cengiz Pehlevan
Translating Theory to Practical Deep Learning: Depthwise Hyperparameter Transfer[Video][CC]
12/05, 11:15am Elad Schneidman
Weizmann Institute of Science
Learning the code of large neural populations with shallow networks and homeostatic random projections[Video][CC]
12/06, 1:30pm Jascha Sohl Dickstein
Question and answer session on AI
12/07, 9:45am Alex Koulakov
Brain evolution as a machine learning algorithm[Video][CC]
12/07, 10:30am Matthieu Wyart
Role of compositionality of data on supervised and unsupervised learning[Embargoed]
12/07, 11:15am Gabriel Kreiman
Some ideas and speculation about robustness, development. and learning in brains and artificial neural networks[Video]
12/08, 1:30pm Alex Atanasov
Tutorial on Neural Scaling Laws[Video][CC]
12/11-12/13 Particle Theory Initiative: PTI23 x DEEPLEARNING23
12/12, 10:30am Mackenzie Mathis
Foundation Models for Neuroscience: a case study with animal pose estimation
12/12, 11:15am Inbar Seroussi
Tel-Aviv University
Can neural network training be thermodynamically optimal?[Video][CC]
12/14, 9:45am Sara Solla
Northwestern University
From Bayes to Gibbs: a thermodynamic theory of learning[Video][CC]
12/14, 10:30am Ard Louis
University of Oxford
Inductive bias towards simplicity and feature learning in DNNs[Video][CC]
12/14, 11:15am Yuhai Tu
Understanding Deep-Learning as a physicist: what would Einstein do?[Video][CC]
12/19, 9:45am Stefano Fusi
The geometry of abstraction in brain and machines[Video][CC]
12/19, 10:30am Bruno Loureiro
ENS Paris
How two-layer neural networks learn, one (giant) step at a time[Video][CC]
12/19, 11:15am Yasaman Bahri
Google DeepMind
A taxonomy for scaling laws in deep neural networks[Video][CC]
12/21, 9:45am Hidenori Tanaka
Harvard / NTT
Experimental Physics of AI: Can Generative Models Imagine?[Video][CC]
12/21, 10:30am Alexander Mathis
Perspectives on deep learning and motor neuroscience[Video][CC]
12/21, 11:15am SueYeon Chung
Theory of Neural Manifolds: A Multi-Level Probe of Representations in Biological and Artificial Neural Networks
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