
Finding online neural update rules by learning to remember
We investigate learning of the online local update rules for neural acti...
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Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
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Shaping Belief States with Generative Environment Models for RL
When agents interact with a complex environment, they must form and main...
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An investigation of modelfree planning
The field of reinforcement learning (RL) is facing increasingly challeng...
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Learning Attractor Dynamics for Generative Memory
A central challenge faced by memory systems is the robust retrieval of a...
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Temporal Difference Variational AutoEncoder
One motivation for learning generative models of environments is to use ...
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Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in modelbased reinforcement learning (RL) is to synthes...
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Variational Intrinsic Control
In this paper we introduce a new unsupervised reinforcement learning met...
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What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
(This paper was written in November 2011 and never published. It is post...
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Neural Autoregressive Distribution Estimation
We present Neural Autoregressive Distribution Estimation (NADE) models, ...
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Towards Conceptual Compression
We introduce a simple recurrent variational autoencoder architecture th...
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OneShot Generalization in Deep Generative Models
Humans have an impressive ability to reason about new concepts and exper...
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Towards Principled Unsupervised Learning
General unsupervised learning is a longstanding conceptual problem in m...
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DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural ...
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MADE: Masked Autoencoder for Distribution Estimation
There has been a lot of recent interest in designing neural network mode...
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Neural Variational Inference and Learning in Belief Networks
Highly expressive directed latent variable models, such as sigmoid belie...
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Fast approximations to structured sparse coding and applications to object classification
We describe a method for fast approximation of sparse coding. The input ...
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Learning Representations by Maximizing Compression
We give an algorithm that learns a representation of data through compre...
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Efficient Learning of Sparse Invariant Representations
We propose a simple and efficient algorithm for learning sparse invarian...
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Behavior and performance of the deep belief networks on image classification
We apply deep belief networks of restricted Boltzmann machines to bags o...
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Karol Gregor
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