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Talks and Workshops
Talk
15:00
Transition to Linearity of Wide Neural Networks
- Chaoyue Liu (Ohio State University)
Talk
15:00
The Information Geometry of Unsupervised Reinforcement Learning
- Benjamin Eysenbach (Carnegie Mellon University & Google Brain)
Talk
14:15
On Einstein's effective viscosity formula
- Antoine Gloria (Sorbonne Université and Université Libre de Bruxelles)
Talk
16:00
Gradient Descent for Deep Neural Networks: New Perspectives from Mean-field and NTK
- Marco Mondelli (Institute of Science and Technology Austria)
Talk
16:00
Convergence of gradient descent for learning deep linear neural networks
- Gabin Maxime Nguegnang (RWTH Aachen University)
Talk
16:00
Generalization Bounds via Convex Analysis
- Gergely Neu (Universitat Pompeu Fabra, Barcelona)
Talk
16:00
General Infomax Agents through World Models
- Danijar Hafner (Google Brain & University of Toronto)
Talk
16:00
Spectral properties of wide neural networks and their implications to the convergence speed of different Gradient Descent algorithms
- Maksim Velikanov (Skolkovo Institute of Science and Technology)