2.9k citations · 3k across the 5 of their papers we have counts for
6 papers · 1 filter
Taming VAEs
Danilo Jimenez Rezende, Fabio Viola
In spite of remarkable progress in deep latent variable generative modeling, training still remains a challenge due to a combination of optimization and generalization issues. In p…
Encoding Spatial Relations from Natural Language
Tiago Ramalho, Tomáš Kočiský, Frederic Besse +5
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fa…
Neural Processes
Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum +4
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP),…
Learning models for visual 3D localization with implicit mapping
Dan Rosenbaum, Frederic Besse, Fabio Viola +2
We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels. The goal is to learn an impl…
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols +3
In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations duri…
Learning and Querying Fast Generative Models for Reinforcement Learning
Lars Buesing, Theophane Weber, Sebastien Racaniere +8
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative mo…