Search Results for author:
Found 4 papers, 2 papers with code
Conditional Driving from Natural Language Instructions
Widespread adoption of self-driving cars will depend not only on their safety but largely on their ability to interact with human users.
Deep Generalized Convolutional Sum-Product Networks
The resulting model is fully probabilistic and versatile, yet efficient and straightforward to apply in practical applications in place of traditional deep nets.
From Pixels to Buildings: End-to-end Probabilistic Deep Networks for Large-scale Semantic Mapping
We introduce TopoNets, end-to-end probabilistic deep networks for modeling semantic maps with structure reflecting the topology of large-scale environments.
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs.
