Energy-based models (EBMs) are experiencing a resurgence of interest in both the physics community and the machine learning community. This article provides an intuitive introduction to EBMs, without requiring any background in machine learning, connecting elementary concepts from physics with basic concepts and tools in generative models, and finally giving a perspective where current research in the field is heading. This article, in its original form, was written as an online lecture note in HTML and Javascript and contains interactive graphics. We recommend the reader to also visit the interactive version
Physics-based free energy simulations enable the rigorous calculation of properties, such as conform...
Ideas originating in physics have informed progress in artificial intelligence and machine learning ...
Designing molecules and materials with desired properties is an important prerequisite for advancing...
Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to ...
Machine learning techniques are being increasingly used as flexible non-linear fitting and predictio...
Energy-Based Models (EBMs) are a class of generative models like Variational Autoencoders, Normalizi...
International audienceMachine learning (ML) encompasses a broad range of algorithms and modeling too...
Presented online via Bluejeans Events on October 13, 2021 at 12:00 p.m.Arthur Gretton is a Professor...
Learnergy: Energy-based Machine Learners Welcome to Learnergy. Did you ever reach a bottleneck in ...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Our knowledge of the fundamental particles of nature and their interactions is summarized by the sta...
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate...
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy ...
Machine learning (ML) models have been widely used in diverse applications of energy systems such as...
Current research in Machine Learning (ML) combines the study of variations on well-established metho...
Physics-based free energy simulations enable the rigorous calculation of properties, such as conform...
Ideas originating in physics have informed progress in artificial intelligence and machine learning ...
Designing molecules and materials with desired properties is an important prerequisite for advancing...
Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to ...
Machine learning techniques are being increasingly used as flexible non-linear fitting and predictio...
Energy-Based Models (EBMs) are a class of generative models like Variational Autoencoders, Normalizi...
International audienceMachine learning (ML) encompasses a broad range of algorithms and modeling too...
Presented online via Bluejeans Events on October 13, 2021 at 12:00 p.m.Arthur Gretton is a Professor...
Learnergy: Energy-based Machine Learners Welcome to Learnergy. Did you ever reach a bottleneck in ...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Our knowledge of the fundamental particles of nature and their interactions is summarized by the sta...
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate...
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy ...
Machine learning (ML) models have been widely used in diverse applications of energy systems such as...
Current research in Machine Learning (ML) combines the study of variations on well-established metho...
Physics-based free energy simulations enable the rigorous calculation of properties, such as conform...
Ideas originating in physics have informed progress in artificial intelligence and machine learning ...
Designing molecules and materials with desired properties is an important prerequisite for advancing...