Miguel Lázaro-Gredilla | DeepAI

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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 ...

0 Miguel Lázaro-Gredilla, et al. ∙

∙ 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...

0 J. Swaroop Guntupalli, et al. ∙

∙ 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...

0 Guangyao Zhou, et al. ∙

∙ 01/31/2023

Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation

Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical...

0 Antoine Dedieu, et al. ∙

∙ 01/24/2023

PushWorld: A benchmark for manipulation planning with tools and movable obstacles

While recent advances in artificial intelligence have achieved human-lev...

0 Ken Kansky, et al. ∙

∙ 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...

0 Guangyao Zhou, et al. ∙

∙ 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 ...

0 Nishad Gothoskar, et al. ∙

∙ 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...

0 Guangyao Zhou, et al. ∙

∙ 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 ...

10 Miguel Lázaro-Gredilla, et al. ∙

∙ 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...

3 Antoine Dedieu, et al. ∙

∙ 06/11/2020

Query Training: Learning and inference for directed and undirected graphical models

Probabilistic graphical models (PGMs) provide a compact representation o...

31 Miguel Lázaro-Gredilla, et al. ∙

∙ 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 ...

0 Nishad Gothoskar, et al. ∙

∙ 03/10/2020

Learning a generative model for robot control using visual feedback

We introduce a novel formulation for incorporating visual feedback in co...

0 Nishad Gothoskar, et al. ∙

∙ 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...

0 Daniel P. Sawyer, et al. ∙

∙ 12/05/2019

Learning undirected models via query training

Typical amortized inference in variational autoencoders is specialized f...

0 Miguel Lázaro-Gredilla, et al. ∙

∙ 05/01/2019

Learning higher-order sequential structure with cloned HMMs

Variable order sequence modeling is an important problem in artificial a...

0 Antoine Dedieu, et al. ∙

∙ 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...

0 Miguel Lázaro-Gredilla, et al. ∙

∙ 08/03/2018

Cortical Microcircuits from a Generative Vision Model

Understanding the information processing roles of cortical circuits is a...

4 Dileep George, et al. ∙

∙ 04/05/2018

Variational Rejection Sampling

Learning latent variable models with stochastic variational inference is...

0 Aditya Grover, et al. ∙

∙ 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...

0 Ken Kansky, et al. ∙

∙ 11/07/2016

Hierarchical compositional feature learning

We introduce the hierarchical compositional network (HCN), a directed ge...

0 Miguel Lázaro-Gredilla, et al. ∙

∙ 03/12/2013

Gaussian Processes for Nonlinear Signal Processing

Gaussian processes (GPs) are versatile tools that have been successfully...

0 Fernando Perez-Cruz, et al. ∙

∙ 08/16/2011

Overlapping Mixtures of Gaussian Processes for the Data Association Problem

In this work we introduce a mixture of GPs to address the data associati...

0 Miguel Lázaro-Gredilla, et al. ∙