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The Theory of Deep Learning - Part I

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 …

Conference on Machine Learning and Physics at IASTU

IASTU Beijing hosts "Machine Learning and Physics" (July 4-6, 2018)

Program on Machine Learning for Quantum Many-Body Physics at KITP

KITP Santa Barbara announces a program on Machine Learning for Quantum Many-Body Physics (January 28 - March 22, 2019)

Workshop Machine Learning for Quantum Many-Body Physics at mpipks

mpipks Dresden hosts the workshop Machine Learning for Quantum Many-Body Physics (June 25-29, 2018)

Machine Learning Topological Defects in the XY Model

Machine learning can detect classical topological defects in materials

Workshop in Machine Learning and Many-Body Physics at KITS

KITS Beijing hosts a workshop in Machine Learning and Many-Body Physics (June 28 - July 7, 2017)

Intro to Machine Learning Talk at KITP

Roger Melko presented an introduction to machine learning for a physics audience at the KITP in Santa Barbara

Tensor Networks: Putting Quantum Wavefunctions into Machine Learning

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.

Machine Learning and Quantum Mechanics

How do quantum mechanics and machine learning fit together?

Quantum Machine Learning Conference at PI

Scientists from academia and industry converge on Waterloo as the Perimeter Institute hosts Quantum Machine Learning (August 5-12, 2016)

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