Physics-Enhanced Gaussian Processes for Learning of Electromechanical Systems
Explore physics-enhanced Gaussian processes for modeling electromechanical systems. Learn about GP-PHS and physics-enhanced variational autoencoders to improve data efficiency and ensure physical correctness.
Challenges and Opportunities for Integrating Physics-Knowledge in Machine Learning Strategies
Explore integrating physics knowledge into machine learning algorithms for improved generalization and physics-consistent predictions. Examine biases and strategies through a friction identification case study.
Pathwise Conditioning and Non-Euclidean Gaussian Processes
Explore advanced Gaussian process techniques for efficient Bayesian optimization, including pathwise conditioning and models on graphs and manifolds, enhancing decision-making in complex settings.
Geometric Probabilistic Models - Tutorial 4
Explore geometric probabilistic models for data-efficient, uncertainty-aware decision-making in applications like drug design and robotics. Learn theory and implementation of Gaussian processes and related techniques.
Gaussian Processes
Explore Gaussian Processes with Daniel Hernández-Lobato, covering key concepts, applications, and advanced techniques in probabilistic machine learning.
A First-Principles Approach to Understanding Deep Learning
Delve into the fundamental principles of deep learning through statistical physics, exploring connections between neural networks, Gaussian processes, and scaling laws with insights from a DeepMind researcher.