4 papers
Partitioned integrators for thermodynamic parameterization of neural networks
Benedict Leimkuhler, Charles Matthews, Tiffany Vlaar
Traditionally, neural networks are parameterized using optimization procedures such as stochastic gradient descent, RMSProp and ADAM. These procedures tend to drive the parameters…
Simulating the stochastic dynamics and cascade failure of power networks
Charles Matthews, Bradly Stadie, Jonathan Weare +2
For large-scale power networks, the failure of particular transmission lines can offload power to other lines and cause self-protection trips to activate, instigating a cascade of…
Langevin Markov Chain Monte Carlo with stochastic gradients
Charles Matthews, Jonathan Weare
Monte Carlo sampling techniques have broad applications in machine learning, Bayesian posterior inference, and parameter estimation. Often the target distribution takes the form of…
MIST: A Simple and Efficient Molecular Dynamics Abstraction Library for Integrator Development
Iain Bethune, Ralf Banisch, Elena Breitmoser +6
We present MIST, the Molecular Integration Simulation Toolkit, a lightweight and efficient software library written in C++ which provides an abstract in- terface to common molecula…