Koray Kavukcuoglu

research

∙ 09/28/2022

Improving alignment of dialogue agents via targeted human judgements

We present Sparrow, an information-seeking dialogue agent trained to be ...

2 Amelia Glaese, et al. ∙

research

∙ 02/08/2022

Competition-Level Code Generation with AlphaCode

Programming is a powerful and ubiquitous problem-solving tool. Developin...

0 Yujia Li, 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

∙ 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...

0 Jean-Bastien Grill, 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

∙ 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...

2 Max Jaderberg, 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

∙ 03/28/2018

Unsupervised Predictive Memory in a Goal-Directed Agent

Animals execute goal-directed behaviours despite the limited range and s...

0 Greg Wayne, 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

∙ 02/05/2018

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

∙ 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

∙ 04/10/2017

Automated Curriculum Learning for Neural Networks

We introduce a method for automatically selecting the path, or syllabus,...

0 Alex Graves, et al. ∙

research

∙ 03/01/2017

Understanding Synthetic Gradients and Decoupled Neural Interfaces

When training neural networks, the use of Synthetic Gradients (SG) allow...

0 Wojciech Marian Czarnecki, et al. ∙

research

∙ 12/01/2016

Interaction Networks for Learning about Objects, Relations and Physics

Reasoning about objects, relations, and physics is central to human inte...

0 Peter W. Battaglia, et al. ∙

research

∙ 11/16/2016

Reinforcement Learning with Unsupervised Auxiliary Tasks

Deep reinforcement learning agents have achieved state-of-the-art result...

0 Max Jaderberg, et al. ∙

research

∙ 11/11/2016

Learning to Navigate in Complex Environments

Learning to navigate in complex environments with dynamic elements is an...

0 Piotr Mirowski, et al. ∙

research

∙ 11/05/2016

Combining policy gradient and Q-learning

Policy gradient is an efficient technique for improving a policy in a re...

0 Brendan O'Donoghue, 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

∙ 08/18/2016

Decoupled Neural Interfaces using Synthetic Gradients

Training directed neural networks typically requires forward-propagating...

0 Max Jaderberg, et al. ∙

research

∙ 06/16/2016

Conditional Image Generation with PixelCNN Decoders

This work explores conditional image generation with a new image density...

0 Aaron van den Oord, et al. ∙

research

∙ 06/15/2016

Strategic Attentive Writer for Learning Macro-Actions

We present a novel deep recurrent neural network architecture that learn...

0 Alexander, et al. ∙

research

∙ 06/13/2016

Matching Networks for One Shot Learning

Learning from a few examples remains a key challenge in machine learning...

0 Oriol Vinyals, et al. ∙

research

∙ 03/28/2016

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

We present a framework for efficient inference in structured image model...

0 S. M. Ali Eslami, et al. ∙

research

∙ 02/08/2016

Exploiting Cyclic Symmetry in Convolutional Neural Networks

Many classes of images exhibit rotational symmetry. Convolutional neural...

0 Sander Dieleman, et al. ∙

research

∙ 02/04/2016

Asynchronous Methods for Deep Reinforcement Learning

We propose a conceptually simple and lightweight framework for deep rein...

0 Volodymyr Mnih, et al. ∙

research

∙ 01/25/2016

Pixel Recurrent Neural Networks

Modeling the distribution of natural images is a landmark problem in uns...

0 Aaron van den Oord, et al. ∙

research

∙ 07/15/2015

Massively Parallel Methods for Deep Reinforcement Learning

We present the first massively distributed architecture for deep reinfor...

0 Arun Nair, 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

∙ 05/20/2015

Weight Uncertainty in Neural Networks

We introduce a new, efficient, principled and backpropagation-compatible...

0 Charles Blundell, et al. ∙

research

∙ 12/24/2014

Multiple Object Recognition with Visual Attention

We present an attention-based model for recognizing multiple objects in ...

0 Jimmy Ba, et al. ∙

research

∙ 06/24/2014

Recurrent Models of Visual Attention

Applying convolutional neural networks to large images is computationall...

0 Volodymyr Mnih, et al. ∙

research

∙ 12/20/2013

Unsupervised Feature Learning by Deep Sparse Coding

In this paper, we propose a new unsupervised feature learning framework,...

0 Yunlong He, et al. ∙

research

∙ 12/19/2013

Playing Atari with Deep Reinforcement Learning

We present the first deep learning model to successfully learn control p...

0 Volodymyr Mnih, et al. ∙

research

∙ 12/01/2012

Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

Pedestrian detection is a problem of considerable practical interest. Ad...

0 Pierre Sermanet, et al. ∙

research

∙ 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...

0 Abhishek Kumar, et al. ∙

research

∙ 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...

0 Koray Kavukcuoglu, et al. ∙

research

∙ 10/03/2010

Convolutional Matching Pursuit and Dictionary Training

Matching pursuit and K-SVD is demonstrated in the translation invariant ...

0 Arthur Szlam, et al. ∙

Koray Kavukcuoglu

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