Search Results for author: Fabio Viola

Found 22 papers, 9 papers with code

Learning to encode spatial relations from natural language

no code implementations ICLR 2019 Tiago Ramalho, Tomas Kocisky‎, Frederic Besse, S. M. Ali Eslami, Gabor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann

Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.

Natural Language Processing

Podracer architectures for scalable Reinforcement Learning

no code implementations13 Apr 2021 Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt

Supporting state-of-the-art AI research requires balancing rapid prototyping, ease of use, and quick iteration, with the ability to deploy experiments at a scale traditionally associated with production systems. Deep learning frameworks such as TensorFlow, PyTorch and JAX allow users to transparently make use of accelerators, such as TPUs and GPUs, to offload the more computationally intensive parts of training and inference in modern deep learning systems.

reinforcement-learning

Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers

no code implementations8 Apr 2020 Leticia Decker, Daniel Leite, Fabio Viola, Daniele Bonacorsi

Evolving granular classifiers are suited to learn from time-varying log streams and, therefore, perform online classification of the severity of anomalies.

Anomaly Detection Classification +1

Neural Communication Systems with Bandwidth-limited Channel

no code implementations30 Mar 2020 Karen Ullrich, Fabio Viola, Danilo Jimenez Rezende

Reliably transmitting messages despite information loss due to a noisy channel is a core problem of information theory.

Consistent Jumpy Predictions for Videos and Scenes

no code implementations ICLR 2019 Ananya Kumar, S. M. Ali Eslami, Danilo Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

3D Scene Reconstruction Video Prediction

TF-Replicator: Distributed Machine Learning for Researchers

1 code implementation1 Feb 2019 Peter Buchlovsky, David Budden, Dominik Grewe, Chris Jones, John Aslanides, Frederic Besse, Andy Brock, Aidan Clark, Sergio Gómez Colmenarejo, Aedan Pope, Fabio Viola, Dan Belov

We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow.

Continuous Control Image Generation +1

Taming VAEs

3 code implementations1 Oct 2018 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.

Consistent Generative Query Networks

1 code implementation ICLR 2019 Ananya Kumar, S. M. Ali Eslami, Danilo J. Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

3D Scene Reconstruction Video Prediction

Neural Processes

12 code implementations4 Jul 2018 Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh

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.

Learning models for visual 3D localization with implicit mapping

1 code implementation4 Jul 2018 Dan Rosenbaum, Frederic Besse, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami

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.

Visual Localization

Encoding Spatial Relations from Natural Language

2 code implementations4 Jul 2018 Tiago Ramalho, Tomáš Kočiský, Frederic Besse, S. M. Ali Eslami, Gábor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann

Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.

Natural Language Processing

Generative Temporal Models with Spatial Memory for Partially Observed Environments

no code implementations ICML 2018 Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola

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 during training or via use as part of an explicit planning mechanism.

Model-based Reinforcement Learning

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