
Institute for Pure & Applied Mathematics (IPAM)
The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.
Episodes


Martha Lewis - Compositional Approaches to Modelling Language and Concepts - IPAM at UCLA

Sean Tull - Towards Compositional Interpretability for XAI - IPAM at UCLA

Mehrnoosh Sadrzadeh - How can large language models learn from humans? - IPAM at UCLA

Tom Griffiths - Understanding human intelligence through human limitations - IPAM at UCLA

David Spivak - Plausible Fiction: Accounting for Actualizing Potential - IPAM at UCLA

Tai-Danae Bradley - An Enriched Category Theory of Language - IPAM at UCLA

Andrew Barron - Honey bee intelligence: thinking with one million neurons - IPAM at UCLA

Christopher Hillar - From McCulloch-Pitts to Retina - IPAM at UCLA

David Wolpert - From Smith’s Invisible Hand to Distributed Optimization and Control - IPAM at UCLA

Sanjoy Dasgupta - Neural machines: From fruit flies to embedded devices - IPAM at UCLA

Misha Belkin - Emergence and grokking in "simple" architectures - IPAM at UCLA

Leena Vankadara - Scaling Insights from Infinite-Width Theory for Next Gen Architecture & Learning

Adityanarayanan Radhakrishnan - How do neural networks learn features from data? - IPAM at UCLA

Mayank Mehta - Dynamics of brain's deep network - IPAM at UCLA

Oliver Eberle - Interpretability for Deep Learning: Theory, Applications and Scientific Insights

Fanny Yang - Surprising phenomena of max-lp-margin classifiers in high dimensions - IPAM at UCLA

Dmitry Krotov - Generative AI models through the lens of Dense Associative Memory - IPAM at UCLA

Blake Bordelon - Infinite limits and scaling laws of neural networks - IPAM at UCLA

Paul Riechers - geometric representation of far future in deep neural networks trained on next-token
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