〈 physics | machine learning 〉

NEWS

Machine Learning in Condensed Matter Physics 2019 at DIPC

Dognostia International Physics Center (DICP) hosts machine learning in condensed matter physics course Aug 26-28

Machine Learning for Quantum Design at PI

Perimeter Institute for Theoretical Physics hosts the conference Machine Learning for Quantum Design (July 8-12, 2019)

Workshop Machine Learning for Quantum Technology at MPL Erlangen

MPL Erlangen hosts the workshop Machine Learning for Quantum Technology (May 8-10, 2019)

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)



BLOG ARTICLES

Neural Autoregressive Distribution Estimators

Modeling states (ground or thermal) in computational physics requires calculating the partition function - an expression that scales exponentially with the number ...

Probing Topological Properties of the 3D Lattice Dimer Model With Neural Networks

Imagine a plane tiled with squares. Such arrangement may be covered with dominoes ...

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 ...

WHO WE ARE

Meet the team of physicists contributing to this blog, working at the intersection of machine learning and quantum physics, across three institutes:
Perimeter Institute: leading research, training and outreach in foundational theoretical physics. The Flatiron Institute: advancing scientific research through computational methods. Vector Institute: driving excellence and leadership in knowledge, creation, and use of artificial intelligence.

These are the papers that we are reading, and the ideas that we are talking about.

    Roger Melko
    Miles Stoudenmire
    Anna Go
    Matt Beach