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Cengiz Pehlevan - 2 stories in mechanistic interpretation of natural & artificial neural computation
Presenter
- Cengiz Pehlevan
October 15, 2024
IPAM
Misha Belkin - Emergence and grokking in "simple" architectures - IPAM at UCLA
Presenter
- Misha Belkin
October 18, 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
Boris Hanin - Neural Network Scaling Limits - IPAM at UCLA
Presenter
- Boris Hanin
October 14, 2024
IPAM
Patrick Shafto - Common Ground in Cooperative Communication - IPAM at UCLA
Presenter
- Patrick Shafto
October 15, 2024
IPAM
Adityanarayanan Radhakrishnan - How do neural networks learn features from data? - IPAM at UCLA
Presenter
- Adityanarayanan Radhakrishnan
October 18, 2024
IPAM
Wu Lin - A framework for designing (non-diagonal) adaptive training methods - IPAM at UCLA
Presenter
- Wu Lin
October 14, 2024
IPAM
Practically Relevant Theory and Theoretically Relevant Practice of Deep Learning
Presenter
- Shai Shalev-Shwartz
May 17, 2016
IMA
Integrating the spectral analysis of neural networks and nonlinear physics for explainability, generalizability, and stability
Presenter
- Pedram Hassanzadeh
November 4, 2022
IMSI
Deep Learning: Approximation of functions by composition
Presenter
- Zuowei Shen
February 9, 2018
IPAM
