16 citations · 43 across the 5 of their papers we have counts for
5 papers
Conditional Driving from Natural Language Instructions
Junha Roh, Chris Paxton, Andrzej Pronobis +2
Widespread adoption of self-driving cars will depend not only on their safety but largely on their ability to interact with human users. Just like human drivers, self-driving cars…
Deep Generalized Convolutional Sum-Product Networks
Jos van de Wolfshaar, Andrzej Pronobis
Sum-Product Networks (SPNs) are hierarchical, graphical models that combine benefits of deep learning and probabilistic modeling. SPNs offer unique advantages to applications deman…
From Pixels to Buildings: End-to-end Probabilistic Deep Networks for Large-scale Semantic Mapping
Kaiyu Zheng, Andrzej Pronobis
We introduce TopoNets, end-to-end probabilistic deep networks for modeling semantic maps with structure reflecting the topology of large-scale environments. TopoNets build a unifie…
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…
Learning Deep Generative Spatial Models for Mobile Robots
Andrzej Pronobis, Rajesh P. N. Rao
We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unl…