7 citations · 20 across the 14 of their papers we have counts for
14 papers
Diffusion Model Predictive Control
Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju +6
We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, a…
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach +7
Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for ex…
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments
Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou +2
Despite their stellar performance on a wide range of tasks, including in-context tasks only revealed during inference, vanilla transformers and variants trained for next-token pred…
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
Mel Vecerik, Carl Doersch, Yi Yang +6
For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the generality to onboard ne…
Graph schemas as abstractions for transfer learning, inference, and planning
J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra +6
Transferring latent structure from one environment or problem to another is a mechanism by which humans and animals generalize with very little data. Inspired by cognitive and neur…
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
Guangyao Zhou, Nishad Gothoskar, Lirui Wang +5
The ability to perceive and understand 3D scenes is crucial for many applications in computer vision and robotics. Inverse graphics is an appealing approach to 3D scene understandi…
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation
Antoine Dedieu, Guangyao Zhou, Dileep George +1
Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical models which express rich statistical dependencies in binary data. Variational inference (VI) has been the…
Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus
Rajkumar Vasudeva Raju, J. Swaroop Guntupalli, Guangyao Zhou +2
Fascinating and puzzling phenomena, such as landmark vector cells, splitter cells, and event-specific representations to name a few, are regularly discovered in the hippocampus. Wi…
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
Guangyao Zhou, Antoine Dedieu, Nishanth Kumar +4
PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalab…
Metropolis Augmented Hamiltonian Monte Carlo
Guangyao Zhou
Hamiltonian Monte Carlo (HMC) is a powerful Markov Chain Monte Carlo (MCMC) method for sampling from complex high-dimensional continuous distributions. However, in many situations…
Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping
Guangyao Zhou, Wolfgang Lehrach, Antoine Dedieu +2
Discrete undirected graphical models, also known as Markov Random Fields (MRFs), can flexibly encode probabilistic interactions of multiple variables, and have enjoyed successful a…
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,…
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Guangyao Zhou
Hamiltonian Monte Carlo (HMC) has emerged as a powerful Markov Chain Monte Carlo (MCMC) method to sample from complex continuous distributions. However, a fundamental limitation of…
Capacities and the Free Passage of Entropic Barriers
Jackson Loper, Guangyao Zhou, Stuart Geman
We propose an approach for estimating the probability that a given small target, among many, will be the first to be reached in a molecular dynamics simulation. Reaching small targ…