Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics
Daniel Kunin and Hidenori Tanaka
Видео Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics канала Physics Meets ML
Видео Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics канала Physics Meets ML
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Anomaly detection in the search for new physics
Insights on gradient-based algorithms in high-dimensional learning
Feature Learning in Infinite-Width Neural Networks
Weak Supervision for the Strong Force
Implicit Regularization in Quantum Tensor Networks
Building symmetries into generative flow models
Machine Learning for Calabi-Yau metrics
Algebraic Neural Networks
Normalizing Flows for scientific applications
Harnessing Data Revolution in Quantum Matter
Science is a verb: adopting the scientific method and best practices in AI research
Fast and Credible Inference with Truncated Marginal Neural Ratio Estimation
Simulation-based inference for astrophysical dark matter searches
Explorations at the Physics ∩ ML Interface
Compositionality of Symmetry in Equivariant Multilayer Perceptrons
Discovering new phases of matter with unsupervised and interpretable support vector machines
Equivariant Neural Fields: A Roadmap Towards Generalizable Neural Representation and Inference
Uncovering the Unknowns of Deep Neural Networks: Challenges and Opportunities
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
Quantum Machine Learning in High Energy Physics