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03/13/2023
Fast exploration and learning of latent graphs with aliased observations
Consider this scenario: an agent navigates a latent graph by performing ...
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02/14/2023
Graph schemas as abstractions for transfer learning, inference, and planning
We propose schemas as a model for abstractions that can be used for rapi...
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02/07/2023
3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics
A central challenge in 3D scene perception via inverse graphics is robus...
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01/31/2023
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation
Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical...
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01/24/2023
PushWorld: A benchmark for manipulation planning with tools and movable obstacles
While recent advances in artificial intelligence have achieved human-lev...
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02/08/2022
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
PGMax is an open-source Python package for easy specification of discret...
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02/08/2022
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model
Visual servoing enables robotic systems to perform accurate closed-loop ...
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12/06/2021
Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping
Discrete undirected graphical models, also known as Markov Random Fields...
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11/03/2021
Perturb-and-max-product: Sampling and learning in discrete energy-based models
Perturb-and-MAP offers an elegant approach to approximately sample from ...
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12/03/2020
Sample-efficient L0-L2 constrained structure learning of sparse Ising models
We consider the problem of learning the underlying graph of a sparse Isi...
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06/11/2020
Query Training: Learning and inference for directed and undirected graphical models
Probabilistic graphical models (PGMs) provide a compact representation o...
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06/11/2020
From proprioception to long-horizon planning in novel environments: A hierarchical RL model
For an intelligent agent to flexibly and efficiently operate in complex ...
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03/10/2020
Learning a generative model for robot control using visual feedback
We introduce a novel formulation for incorporating visual feedback in co...
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02/10/2020
A Model of Fast Concept Inference with Object-Factorized Cognitive Programs
The ability of humans to quickly identify general concepts from a handfu...
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12/05/2019
Learning undirected models via query training
Typical amortized inference in variational autoencoders is specialized f...
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05/01/2019
Learning higher-order sequential structure with cloned HMMs
Variable order sequence modeling is an important problem in artificial a...
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12/06/2018
Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs
Humans can infer concepts from image pairs and apply those in the physic...
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08/03/2018
Cortical Microcircuits from a Generative Vision Model
Understanding the information processing roles of cortical circuits is a...
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04/05/2018
Variational Rejection Sampling
Learning latent variable models with stochastic variational inference is...
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06/14/2017
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
The recent adaptation of deep neural network-based methods to reinforcem...
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11/07/2016
Hierarchical compositional feature learning
We introduce the hierarchical compositional network (HCN), a directed ge...
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03/12/2013
Gaussian Processes for Nonlinear Signal Processing
Gaussian processes (GPs) are versatile tools that have been successfully...
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08/16/2011