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08/14/2023
Bayesian Flow Networks
This paper introduces Bayesian Flow Networks (BFNs), a new class of gene...
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06/12/2020
A Practical Sparse Approximation for Real Time Recurrent Learning
Current methods for training recurrent neural networks are based on back...
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04/06/2018
Associative Compression Networks for Representation Learning
This paper introduces Associative Compression Networks (ACNs), a new fra...
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04/06/2018
Associative Compression Networks
This paper introduces Associative Compression Networks (ACNs), a new fra...
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04/05/2018
The Kanerva Machine: A Generative Distributed Memory
We present an end-to-end trained memory system that quickly adapts to ne...
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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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06/30/2017
Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametr...
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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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10/31/2016
Neural Machine Translation in Linear Time
We present a novel neural network for processing sequences. The ByteNet ...
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10/27/2016
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Neural networks augmented with external memory have the ability to learn...
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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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07/19/2016
Stochastic Backpropagation through Mixture Density Distributions
The ability to backpropagate stochastic gradients through continuous lat...
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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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06/10/2016
Memory-Efficient Backpropagation Through Time
We propose a novel approach to reduce memory consumption of the backprop...
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03/29/2016
Adaptive Computation Time for Recurrent Neural Networks
This paper introduces Adaptive Computation Time (ACT), an algorithm that...
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02/09/2016
Associative Long Short-Term Memory
We investigate a new method to augment recurrent neural networks with ex...
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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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07/06/2015
Grid Long Short-Term Memory
This paper introduces Grid Long Short-Term Memory, a network of LSTM cel...
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02/16/2015
DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural ...
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10/20/2014
Neural Turing Machines
We extend the capabilities of neural networks by coupling them to extern...
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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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12/19/2013
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control p...
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08/04/2013
Generating Sequences With Recurrent Neural Networks
This paper shows how Long Short-term Memory recurrent neural networks ca...
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03/22/2013
Speech Recognition with Deep Recurrent Neural Networks
Recurrent neural networks (RNNs) are a powerful model for sequential dat...
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11/14/2012
Sequence Transduction with Recurrent Neural Networks
Many machine learning tasks can be expressed as the transformation---or ...
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04/21/2008
Phoneme recognition in TIMIT with BLSTM-CTC
We compare the performance of a recurrent neural network with the best r...
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05/14/2007