1 code implementation • 28 Jan 2022 • Ryan R. Strauss, Junier B. Oliva
Arbitrary conditioning is an important problem in unsupervised learning, where we seek to model the conditional densities $p(\mathbf{x}_u \mid \mathbf{x}_o)$ that underly some data, for all possible non-intersecting subsets $o, u \subset \{1, \dots , d\}$.
1 code implementation • NeurIPS 2021 • Ryan R. Strauss, Junier B. Oliva
A more general and useful problem is arbitrary conditional density estimation, which aims to model any possible conditional distribution over a set of covariates, reflecting the more realistic setting of inference based on prior knowledge.
no code implementations • 6 Aug 2020 • Robert Solli, Daniel Bazin, Michelle P. Kuchera, Ryan R. Strauss, Morten Hjorth-Jensen
We also explore the application of clustering the latent space of autoencoder neural networks for event separation.
2 code implementations • 21 Oct 2018 • Michelle P. Kuchera, Raghuram Ramanujan, Jack Z. Taylor, Ryan R. Strauss, Daniel Bazin, Joshua Bradt, Ruiming Chen
We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University.