Tsinghua Workshop on Machine Learning in Geometry and Physics 2018

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A Generalized Construction of Calabi-Yau Manifolds and Mirror Symmetry

Algebraic geometry of the restricted Boltzmann machine

Deep Learning and AdS/CFT

Detection of phase transition via convolutional neural networks

Faster exploration of parameter space in supersymmetry and string theory using machine learning

Learning and Lie Groups

Machine learning for parton distribution determination

Neural Program Synthesis and Neural Automated Theorem Proving, via Curry-Howard Correspondence

On Finding Small Cosmological Constants with Deep Reinforcement Learning

Patterns in Calabi-Yau Threefolds

Quantum Computers and Machine Learning

Real-space renormalization group

Reinforcement learning in the string landscape

Renormalization and hierarchical knowledge representations

Reverse engineering Hamiltonian from spectrum

Tensor network from quantum simulations to machine learning

Tensor Network Holography and Deep Learning

The Machine Learning Role on High Energy Physics: A theoretical view

The Nelson-Seiberg theorem, its extensions, string realizations, and possible machine learning applications

Topological Data Analysis for Cosmology and String Theory

Toward reduction of autocorrelation in HMC by machine learning

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10/06/2018

11/06/2018

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14/06/2018

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