41 citations · 115 across the 6 of their papers we have counts for
6 papers
Learning and Inference in Sparse Coding Models with Langevin Dynamics
Michael Y. -S. Fang, Mayur Mudigonda, Ryan Zarcone +2
We describe a stochastic, dynamical system capable of inference and learning in a probabilistic latent variable model. The most challenging problem in such models - sampling the po…
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Stephen Tian, Frederik Ebert, Dinesh Jayaraman +4
Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging.…
Novel deep learning methods for track reconstruction
Steven Farrell, Paolo Calafiura, Mayur Mudigonda +11
For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew…
Exascale Deep Learning for Climate Analytics
Thorsten Kurth, Sean Treichler, Joshua Romero +9
We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipel…
A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
Andrew B. Berger, Mayur Mudigonda, Michael R. DeWeese +1
In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are…
Hamiltonian Monte Carlo Without Detailed Balance
Jascha Sohl-Dickstein, Mayur Mudigonda, Michael R. DeWeese
We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection for typical hyperparameters. In situations that would normally lead to rejection…