151 citations · 165 across the 7 of their papers we have counts for
14 papers
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…
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu +4
Visual servoing enables robotic systems to perform accurate closed-loop control, which is required in many applications. However, existing methods either require precise calibratio…
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…
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models
Antoine Dedieu, Miguel Lázaro-Gredilla, Dileep George
We consider the problem of learning the underlying graph of a sparse Ising model with nodes from i.i.d. samples. The most recent and best performing approaches combine an e…
From proprioception to long-horizon planning in novel environments: A hierarchical RL model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Dileep George
For an intelligent agent to flexibly and efficiently operate in complex environments, they must be able to reason at multiple levels of temporal, spatial, and conceptual abstractio…
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,…