Karen Simonyan

research

∙ 04/29/2022

Flamingo: a Visual Language Model for Few-Shot Learning

Building models that can be rapidly adapted to numerous tasks using only...

7 Jean-Baptiste Alayrac, et al. ∙

research

∙ 03/29/2022

Training Compute-Optimal Large Language Models

We investigate the optimal model size and number of tokens for training ...

6 Jordan Hoffmann, et al. ∙

research

∙ 02/22/2022

Hierarchical Perceiver

General perception systems such as Perceivers can process arbitrary moda...

2 Joao Carreira, et al. ∙

research

∙ 02/02/2022

Unified Scaling Laws for Routed Language Models

The performance of a language model has been shown to be effectively mod...

2 Aidan Clark, et al. ∙

research

∙ 12/08/2021

Improving language models by retrieving from trillions of tokens

We enhance auto-regressive language models by conditioning on document c...

8 Sebastian Borgeaud, et al. ∙

research

∙ 04/12/2021

Machine Translation Decoding beyond Beam Search

Beam search is the go-to method for decoding auto-regressive machine tra...

0 Rémi Leblond, et al. ∙

research

∙ 04/02/2021

Skillful Precipitation Nowcasting using Deep Generative Models of Radar

Precipitation nowcasting, the high-resolution forecasting of precipitati...

6 Suman Ravuri, et al. ∙

research

∙ 03/10/2021

Variable-rate discrete representation learning

Semantically meaningful information content in perceptual signals is usu...

0 Sander Dieleman, et al. ∙

research

∙ 02/11/2021

High-Performance Large-Scale Image Recognition Without Normalization

Batch normalization is a key component of most image classification mode...

0 Andrew Brock, et al. ∙

research

∙ 06/12/2020

AlgebraNets

Neural networks have historically been built layerwise from the set of f...

0 Jordan Hoffmann, et al. ∙

research

∙ 06/12/2020

A Practical Sparse Approximation for Real Time Recurrent Learning

Current methods for training recurrent neural networks are based on back...

4 Jacob Menick, et al. ∙

research

∙ 06/05/2020

End-to-End Adversarial Text-to-Speech

Modern text-to-speech synthesis pipelines typically involve multiple pro...

0 Jeff Donahue, et al. ∙

research

∙ 04/06/2020

Evolving Normalization-Activation Layers

Normalization layers and activation functions are critical components in...

9 Hanxiao Liu, et al. ∙

research

∙ 03/09/2020

Transformation-based Adversarial Video Prediction on Large-Scale Data

Recent breakthroughs in adversarial generative modeling have led to mode...

11 Pauline Luc, et al. ∙

research

∙ 12/02/2019

LOGAN: Latent Optimisation for Generative Adversarial Networks

Training generative adversarial networks requires balancing of delicate ...

6 Yan Wu, et al. ∙

research

∙ 11/21/2019

Fast Sparse ConvNets

Historically, the pursuit of efficient inference has been one of the dri...

0 Erich Elsen, et al. ∙

research

∙ 11/19/2019

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Constructing agents with planning capabilities has long been one of the ...

23 Julian Schrittwieser, et al. ∙

research

∙ 09/25/2019

High Fidelity Speech Synthesis with Adversarial Networks

Generative adversarial networks have seen rapid development in recent ye...

0 Mikołaj Bińkowski, et al. ∙

research

∙ 09/25/2019

Off-Policy Actor-Critic with Shared Experience Replay

We investigate the combination of actor-critic reinforcement learning al...

0 Simon Schmitt, et al. ∙

research

∙ 07/15/2019

Efficient Video Generation on Complex Datasets

Generative models of natural images have progressed towards high fidelit...

0 Aidan Clark, et al. ∙

research

∙ 07/04/2019

Large Scale Adversarial Representation Learning

Adversarially trained generative models (GANs) have recently achieved co...

3 Jeff Donahue, et al. ∙

research

∙ 06/07/2019

Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods

In this work we show that Evolution Strategies (ES) are a viable method ...

9 Karel Lenc, et al. ∙

research

∙ 03/06/2019

Hierarchical Autoregressive Image Models with Auxiliary Decoders

Autoregressive generative models of images tend to be biased towards cap...

0 Jeffrey De Fauw, et al. ∙

research

∙ 03/04/2019

The StreetLearn Environment and Dataset

Navigation is a rich and well-grounded problem domain that drives progre...

8 Piotr Mirowski, et al. ∙

research

∙ 09/28/2018

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Despite recent progress in generative image modeling, successfully gener...

2 Andrew Brock, et al. ∙

research

∙ 08/10/2018

This Time with Feeling: Learning Expressive Musical Performance

Music generation has generally been focused on either creating scores or...

0 Sageev Oore, et al. ∙

research

∙ 06/26/2018

The challenge of realistic music generation: modelling raw audio at scale

Realistic music generation is a challenging task. When building generati...

6 Sander Dieleman, et al. ∙

research

∙ 06/24/2018

DARTS: Differentiable Architecture Search

This paper addresses the scalability challenge of architecture search by...

0 Hanxiao Liu, et al. ∙

research

∙ 03/31/2018

Learning to Navigate in Cities Without a Map

Navigating through unstructured environments is a basic capability of in...

0 Piotr Mirowski, et al. ∙

research

∙ 03/10/2018

Kickstarting Deep Reinforcement Learning

We present a method for using previously-trained 'teacher' agents to kic...

0 Simon Schmitt, et al. ∙

research

∙ 02/23/2018

Efficient Neural Audio Synthesis

Sequential models achieve state-of-the-art results in audio, visual and ...

0 Nal Kalchbrenner, et al. ∙

research

∙ 02/13/2018

Learning to Search with MCTSnets

Planning problems are among the most important and well-studied problems...

0 Arthur Guez, et al. ∙

research

∙ 02/05/2018

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

In this work we aim to solve a large collection of tasks using a single ...

0 Lasse Espeholt, et al. ∙

research

∙ 12/05/2017

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

The game of chess is the most widely-studied domain in the history of ar...

0 David Silver, et al. ∙

research

∙ 11/28/2017

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

The recently-developed WaveNet architecture is the current state of the ...

0 Aaron van den Oord, et al. ∙

research

∙ 11/27/2017

Population Based Training of Neural Networks

Neural networks dominate the modern machine learning landscape, but thei...

0 Max Jaderberg, et al. ∙

research

∙ 11/01/2017

Hierarchical Representations for Efficient Architecture Search

We explore efficient neural architecture search methods and present a si...

0 Hanxiao Liu, et al. ∙

research

∙ 08/16/2017

StarCraft II: A New Challenge for Reinforcement Learning

This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...

0 Oriol Vinyals, et al. ∙

research

∙ 05/19/2017

The Kinetics Human Action Video Dataset

We describe the DeepMind Kinetics human action video dataset. The datase...

0 Will Kay, et al. ∙

research

∙ 04/05/2017

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

Generative models in vision have seen rapid progress due to algorithmic ...

0 Jesse Engel, et al. ∙

research

∙ 10/31/2016

Neural Machine Translation in Linear Time

We present a novel neural network for processing sequences. The ByteNet ...

0 Nal Kalchbrenner, et al. ∙

research

∙ 10/03/2016

Video Pixel Networks

We propose a probabilistic video model, the Video Pixel Network (VPN), t...

0 Nal Kalchbrenner, et al. ∙

research

∙ 09/12/2016

WaveNet: A Generative Model for Raw Audio

This paper introduces WaveNet, a deep neural network for generating raw ...

0 Aaron van den Oord, et al. ∙

research

∙ 07/01/2015

Natural Neural Networks

We introduce Natural Neural Networks, a novel family of algorithms that ...

0 Guillaume Desjardins, et al. ∙

research

∙ 06/05/2015

Spatial Transformer Networks

Convolutional Neural Networks define an exceptionally powerful class of ...

0 Max Jaderberg, et al. ∙

research

∙ 12/18/2014

Deep Structured Output Learning for Unconstrained Text Recognition

We develop a representation suitable for the unconstrained recognition o...

0 Max Jaderberg, et al. ∙

research

∙ 12/04/2014

Reading Text in the Wild with Convolutional Neural Networks

In this work we present an end-to-end system for text spotting -- locali...

0 Max Jaderberg, et al. ∙

research

∙ 09/04/2014

Very Deep Convolutional Networks for Large-Scale Image Recognition

In this work we investigate the effect of the convolutional network dept...

0 Karen Simonyan, et al. ∙

research

∙ 07/17/2014

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by...

0 Ken Chatfield, et al. ∙

research

∙ 06/09/2014

Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition

In this work we present a framework for the recognition of natural scene...

0 Max Jaderberg, et al. ∙

Karen Simonyan

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