Workshops

Towards a Unified Machine Learning Model of Molecular Chemical Space

Graph Convolutional Neural Networks for Molecule Generation

Physics-Informed (and -informative) Generative Modelling in Astronomy

A primer on normalizing flows

Deep Generative Learning for Physics Many-Body Systems

New horizons in quantum biology: Learning complexity, emergence, and coherence in living matter

De Novo Drug Design with Deep Reinforcement Learning

Deep Generative Models for Molecular Graphs

Reinforcement learning for molecular generation

Coarse graining autoencoders and evolutionary learning of atomistic potentials

Generative models for molecules.

Automating Science using Robot Scientists

Recent advances in Derivative-Free Optimization and its connection to reinforcement learning.

Automatic Feature Extraction from Hyperspectral Imagery using Deep Recurrent Neural Networks

A Numerical Analysis Perspective on Deep Neural Networks

Generative models for lattice quantum field theory

Machine-learning for materials and physics discovery through symbolic regression and kernel methods

Breaking Bad: Recent Advances from Function Space Characterization of Neural Nets with Implications for Physical Applications

Generalizing Hamiltonian Monte Carlo with Neural Networks

Hunting for dark matter substructures in strong lensing with neural networks.

Coarse-graining molecular systems by spectral matching.

A diversified machine learning strategy for predicting and understanding molecular melting points.

Likelihood free generative modeling for high energy physics

Euclidean Neural Networks* for Emulating Ab Initio Calculations and Generating Atomic Geometries *also called Tensor Field Networks and 3D Steerable CNNs

Simulating quantum circuits with neural machine translation

Generative and variational modeling for quantum many-body physics

Solving Quantum Many-Body Problems with Deep Neural Networks