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Tutorial
Mon 1:00 Pay Attention to What You Need: Do Structural Priors Still Matter in the Age of Billion Parameter Models?
Irina Higgins · Antonia Creswell · Seb Racanière
Tutorial
Mon 1:00 Why do we Need Structure and Where does it Come From?
Irina Higgins
Tutorial
Mon 2:00 Symmetries
Sébastien Racanière
Tutorial
Mon 9:35 Physics-Informed Inductive Biases in Deep Learning
Miles Cranmer · Shirley Ho
Tutorial
Mon 10:50 Accelerating Simulations in Physics with Deep Learning
Miles Cranmer · Shirley Ho
Tutorial
Mon 12:05 The Problem with Deep Learning for Physics (and how to fix it)
Miles Cranmer · Shirley Ho
Panel
Mon 23:00 The Consequences of Massive Scaling in Machine Learning
Noah Goodman · Melanie Mitchell · Joelle Pineau · Oriol Vinyals · Jared Kaplan
Oral
Tue 0:00 E(n) Equivariant Normalizing Flows
Victor Garcia Satorras · Emiel Hoogeboom · Fabian Fuchs · Ingmar Posner · Max Welling
Oral
Tue 0:00 Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Carles Domingo i Enrich · Youssef Mroueh
Oral
Tue 0:00 Framing RNN as a kernel method: A neural ODE approach
Adeline Fermanian · Pierre Marion · Jean-Philippe Vert · Gérard Biau
Oral
Tue 0:20 Online Variational Filtering and Parameter Learning
Andrew Campbell · Yuyang Shi · Thomas Rainforth · Arnaud Doucet
Oral
Tue 0:20 A Universal Law of Robustness via Isoperimetry
Sebastien Bubeck · Mark Sellke