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Found 4 papers, 3 papers with code
Posterior Matching for Arbitrary Conditioning
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 $o, u \subset \{1, \dots , d\}$.
Arbitrary Conditional Distributions with Energy
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.
Unsupervised Learning for Identifying Events in Active Target Experiments
We also explore the application of clustering the latent space of autoencoder neural networks for event separation.
Machine Learning Methods for Track Classification in the AT-TPC
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.
