154 citations · 266 across the 20 of their papers we have counts for
3 papers · 1 filter
Hyper-Universal Policy Approximation: Learning to Generate Actions from a Single Image using Hypernets
Dimitrios C. Gklezakos, Rishi Jha, Rajesh P. N. Rao
Inspired by Gibson's notion of object affordances in human vision, we ask the question: how can an agent learn to predict an entire action policy for a novel object or environment…
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
Kaiyu Zheng, Andrzej Pronobis, Rajesh P. N. Rao
We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expre…
Large Margin Boltzmann Machines and Large Margin Sigmoid Belief Networks
Xu Miao, Rajesh P. N. Rao
Current statistical models for structured prediction make simplifying assumptions about the underlying output graph structure, such as assuming a low-order Markov chain, because ex…