151 citations · 307 across the 12 of their papers we have counts for
4 papers · 1 filter
Perturb-and-max-product: Sampling and learning in discrete energy-based models
Miguel Lazaro-Gredilla, Antoine Dedieu, Dileep George
Perturb-and-MAP offers an elegant approach to approximately sample from a energy-based model (EBM) by computing the maximum-a-posteriori (MAP) configuration of a perturbed version…
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…
Variational Rejection Sampling
Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla +2
Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the grad…
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil Lawrence
In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike sever…