Seminário Matemática, Física & Aprendizagem Automática – Dan Roberts
“The Principles of Deep Learning Theory”. - 17h...
“The Principles of Deep Learning Theory”. - 17h...
“Provable Representation Learning” - 17h...
“Deep neural networks have an inbuilt Occam's razor” - 14h...
“Computational Imaging: Reconciling Physical and Learned Models” - 14h...
“Efficient and Modular Implicit Differentiation” - 14h...
“Partition-based formulations for mixed-integer optimization of trained ReLU neural networks” - 14h...
“Policy Optimization in Reinforcement Learning: A Tale of Preconditioning and Regularization” - 14h...
“Physics Aware Machine Learning for the Earth Sciences” - 14h...
“Learning-Based Actuator Placement and Receding Horizon Control for Security against Actuation Attacks” - 14h ...
“Machine Learning and Inverse Problems: Deeper and More Robust” - 14h...
“Two mathematical lessons of deep learning” - 18h...
“Robot Learning - Quo Vadis?” - 14h...
“Scaling Optimal Transport for High dimensional Learning” - 18h...
“Two-time scale stochastic approximation for reinforcement learning with linear function approximation” - 14h...
“Machine learning for Fluid Mechanics” - 18h...
“Machine Learning of Robot Skills” - 18h...
“Model based control design combining Lyapunov and optimization tools: Examples in the area of motion control of autonomous robotic vehicles” ...
“Information-theoretic bounds on quantum advantage in machine learning” - 18h...