82 citations · 128 across the 6 of their papers we have counts for
6 papers
Posterior Matching for Arbitrary Conditioning
Ryan R. Strauss, Junier B. Oliva
Arbitrary conditioning is an important problem in unsupervised learning, where we seek to model the conditional densities that underly some data…
Arbitrary Conditional Distributions with Energy
Ryan R. Strauss, Junier B. Oliva
Modeling distributions of covariates, or density estimation, is a core challenge in unsupervised learning. However, the majority of work only considers the joint distribution, whic…
Machine learning-based event generator for electron-proton scattering
Y. Alanazi, P. Ambrozewicz, M. Battaglieri +11
We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to constru…
Unsupervised Learning for Identifying Events in Active Target Experiments
Robert Solli, Daniel Bazin, Michelle P. Kuchera +2
This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection…
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Yasir Alanazi, N. Sato, Tianbo Liu +9
We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical as…
Machine Learning Methods for Track Classification in the AT-TPC
Michelle P. Kuchera, Raghuram Ramanujan, Jack Z. Taylor +4
We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at M…