Machine Learning Working Group Meeting




In this session, Bob Carpenter will present the following paper:Hamiltonian Graph Networks with ODE Integrators

We are currently running a series on "Learning & sampling physical models”. A list of potential references is included below, suggestions of others are more than welcome. Anyone interested in presenting for the series, please email Marylou Gabrié.

** References: - GENERALIZING HAMILTONIAN MONTE CARLO WITH NEURAL NETWORKS Equivariant Flows: sampling configurations for multi-body systems with symmetric energies Markov Chain Monte Carlo and variational inference: Bridging the gap Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks Learning with rare data: Using active importance sampling to optimize objectives dominated by rare events the predictive power of static structure in glassy systems

Sara Mejias Gonzalez
The agenda of this meeting is empty