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09/28/2022
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be ...
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02/08/2022
Competition-Level Code Generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool. Developin...
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02/02/2022
Unified Scaling Laws for Routed Language Models
The performance of a language model has been shown to be effectively mod...
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06/13/2020
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-su...
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03/04/2019
The StreetLearn Environment and Dataset
Navigation is a rich and well-grounded problem domain that drives progre...
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07/03/2018
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learnin...
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03/31/2018
Learning to Navigate in Cities Without a Map
Navigating through unstructured environments is a basic capability of in...
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03/28/2018
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and s...
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02/23/2018
Efficient Neural Audio Synthesis
Sequential models achieve state-of-the-art results in audio, visual and ...
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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 ...
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11/28/2017
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
The recently-developed WaveNet architecture is the current state of the ...
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11/27/2017
Population Based Training of Neural Networks
Neural networks dominate the modern machine learning landscape, but thei...
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11/01/2017
Hierarchical Representations for Efficient Architecture Search
We explore efficient neural architecture search methods and present a si...
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04/10/2017
Automated Curriculum Learning for Neural Networks
We introduce a method for automatically selecting the path, or syllabus,...
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03/01/2017
Understanding Synthetic Gradients and Decoupled Neural Interfaces
When training neural networks, the use of Synthetic Gradients (SG) allow...
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12/01/2016
Interaction Networks for Learning about Objects, Relations and Physics
Reasoning about objects, relations, and physics is central to human inte...
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11/16/2016
Reinforcement Learning with Unsupervised Auxiliary Tasks
Deep reinforcement learning agents have achieved state-of-the-art result...
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11/11/2016
Learning to Navigate in Complex Environments
Learning to navigate in complex environments with dynamic elements is an...
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11/05/2016
Combining policy gradient and Q-learning
Policy gradient is an efficient technique for improving a policy in a re...
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10/31/2016
Neural Machine Translation in Linear Time
We present a novel neural network for processing sequences. The ByteNet ...
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10/03/2016
Video Pixel Networks
We propose a probabilistic video model, the Video Pixel Network (VPN), t...
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09/12/2016
WaveNet: A Generative Model for Raw Audio
This paper introduces WaveNet, a deep neural network for generating raw ...
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08/18/2016
Decoupled Neural Interfaces using Synthetic Gradients
Training directed neural networks typically requires forward-propagating...
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06/16/2016
Conditional Image Generation with PixelCNN Decoders
This work explores conditional image generation with a new image density...
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06/15/2016
Strategic Attentive Writer for Learning Macro-Actions
We present a novel deep recurrent neural network architecture that learn...
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06/13/2016
Matching Networks for One Shot Learning
Learning from a few examples remains a key challenge in machine learning...
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03/28/2016
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
We present a framework for efficient inference in structured image model...
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02/08/2016
Exploiting Cyclic Symmetry in Convolutional Neural Networks
Many classes of images exhibit rotational symmetry. Convolutional neural...
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02/04/2016
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep rein...
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01/25/2016
Pixel Recurrent Neural Networks
Modeling the distribution of natural images is a landmark problem in uns...
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07/15/2015
Massively Parallel Methods for Deep Reinforcement Learning
We present the first massively distributed architecture for deep reinfor...
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07/01/2015
Natural Neural Networks
We introduce Natural Neural Networks, a novel family of algorithms that ...
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06/05/2015
Spatial Transformer Networks
Convolutional Neural Networks define an exceptionally powerful class of ...
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05/20/2015
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible...
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12/24/2014
Multiple Object Recognition with Visual Attention
We present an attention-based model for recognizing multiple objects in ...
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06/24/2014
Recurrent Models of Visual Attention
Applying convolutional neural networks to large images is computationall...
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12/20/2013
Unsupervised Feature Learning by Deep Sparse Coding
In this paper, we propose a new unsupervised feature learning framework,...
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12/19/2013
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control p...
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12/01/2012
Pedestrian Detection with Unsupervised Multi-Stage Feature Learning
Pedestrian detection is a problem of considerable practical interest. Ad...
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06/27/2012
A Binary Classification Framework for Two-Stage Multiple Kernel Learning
With the advent of kernel methods, automating the task of specifying a s...
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10/18/2010
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Adaptive sparse coding methods learn a possibly overcomplete set of basi...
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10/03/2010