Why do modern deep neural networks (DNNs) perform so well on previously unseen test data, even when their number of weights is much larger than the number of data points in the training set? This question keeps puzzling many theorists and practicioners doing Deep Learning (DL), in particular those who …
IASTU Beijing hosts "Machine Learning and Physics" (July 4-6, 2018)
KITP Santa Barbara announces a program on Machine Learning for Quantum Many-Body Physics (January 28 - March 22, 2019)
mpipks Dresden hosts the workshop Machine Learning for Quantum Many-Body Physics (June 25-29, 2018)
Machine learning can detect classical topological defects in materials
KITS Beijing hosts a workshop in Machine Learning and Many-Body Physics (June 28 - July 7, 2017)
Roger Melko presented an introduction to machine learning for a physics audience at the KITP in Santa Barbara
Tensor networks are a powerful tool for compressing quantum wavefunctions developed in the physics community. Parameterizing a special class of models with tensor networks brings the full power of tensor networks to machine learning tasks.
How do quantum mechanics and machine learning fit together?
Scientists from academia and industry converge on Waterloo as the Perimeter Institute hosts Quantum Machine Learning (August 5-12, 2016)