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06/22/2021
Towards Biologically Plausible Convolutional Networks
Convolutional networks are ubiquitous in deep learning. They are particu...
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10/28/2020
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
The instability of Generative Adversarial Network (GAN) training has fre...
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11/13/2019
Compressive Transformers for Long-Range Sequence Modelling
We present the Compressive Transformer, an attentive sequence model whic...
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10/07/2019
Meta-Learning Deep Energy-Based Memory Models
We study the problem of learning associative memory – a system which is ...
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09/27/2019
Automated curricula through setter-solver interactions
Reinforcement learning algorithms use correlations between policies and ...
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07/15/2019
What does it mean to understand a neural network?
We can define a neural network that can learn to recognize objects in le...
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06/10/2019
Meta-Learning Neural Bloom Filters
There has been a recent trend in training neural networks to replace dat...
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12/05/2018
Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction
Deep reinforcement learning (RL) algorithms have made great strides in r...
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11/28/2018
Experience Replay for Continual Learning
Continual learning is the problem of learning new tasks or knowledge whi...
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03/27/2018
Fast Parametric Learning with Activation Memorization
Neural networks trained with backpropagation often struggle to identify ...
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11/11/2016
Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic...
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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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02/04/2016
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep rein...
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09/09/2015
