6 citations · 11 across the 4 of their papers we have counts for
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stat.ML2020
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar +3
Probabilistic graphical models (PGMs) provide a compact representation of knowledge that can be queried in a flexible way: after learning the parameters of a graphical model once,…
stat.ML2019★ 6 cited
Learning higher-order sequential structure with cloned HMMs
Antoine Dedieu, Nishad Gothoskar, Scott Swingle +3
Variable order sequence modeling is an important problem in artificial and natural intelligence. While overcomplete Hidden Markov Models (HMMs), in theory, have the capacity to rep…