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Found 8 papers, 2 papers with code
Fast exploration and learning of latent graphs with aliased observations
We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic.
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation
We evaluate both approaches on several benchmarks where VI is the state-of-the-art and show that our method (a) achieves better test performance than Ji et al. (2020) for learning noisy-OR BNs with hierarchical latent structures on large sparse real datasets; (b) recovers a higher number of ground truth parameters than Buhai et al. (2020) from cluttered synthetic scenes; and (c) solves the 2D blind deconvolution problem from Lazaro-Gredilla et al. (2021) and variant - including binary matrix factorization - while VI catastrophically fails and is up to two orders of magnitude slower.
PushWorld: A benchmark for manipulation planning with tools and movable obstacles
We provide a benchmark of more than 200 PushWorld puzzles in PDDL and in an OpenAI Gym environment.
Perturb-and-max-product: Sampling and learning in discrete energy-based models
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 of the model.
Learning undirected models via query training
We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference.
Cortical Microcircuits from a Generative Vision Model
Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence.
Variational Rejection Sampling
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 gradient estimates.
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression.
