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Adityanarayanan Radhakrishnan - How do neural networks learn features from data? - IPAM at UCLA
Presenter
- Adityanarayanan Radhakrishnan
October 18, 2024
IPAM
Leena Vankadara - Scaling Insights from Infinite-Width Theory for Next Gen Architecture & Learning
Presenter
- Leena Vankadara
October 18, 2024
IPAM
Misha Belkin - Emergence and grokking in "simple" architectures - IPAM at UCLA
Presenter
- Misha Belkin
October 18, 2024
IPAM
Dmitry Krotov - Generative AI models through the lens of Dense Associative Memory - IPAM at UCLA
Presenter
- Dmitry Krotov
October 17, 2024
IPAM
Oliver Eberle - Interpretability for Deep Learning: Theory, Applications and Scientific Insights
Presenter
- Oliver Eberle
October 17, 2024
IPAM
Mayank Mehta - Dynamics of brain's deep network - IPAM at UCLA
Presenter
- Mayank Mehta
October 17, 2024
IPAM
Mauro Maggioni - On exploiting compositional structure: one bit of theory and one application
Presenter
- Mauro Maggioni
October 17, 2024
IPAM
Dan Roy - Size of Teachers as Measure of Data Complexity: PAC-Bayes Excess Risk Bounds & Scaling Law
Presenter
- Dan Roy
October 16, 2024
IPAM
Shaowei Lin - Singular Learning, Relative Information and the Dual Numbers - IPAM at UCLA
Presenter
- Shaowei Lin
October 16, 2024
IPAM
Paul Riechers - geometric representation of far future in deep neural networks trained on next-token
Presenter
- Paul Riechers
October 16, 2024
IPAM
Blake Bordelon - Infinite limits and scaling laws of neural networks - IPAM at UCLA
Presenter
- Blake Bordelon
October 16, 2024
IPAM
Cengiz Pehlevan - 2 stories in mechanistic interpretation of natural & artificial neural computation
Presenter
- Cengiz Pehlevan
October 15, 2024
IPAM