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7 papers · 1 filter
Multiple Object Recognition with Visual Attention
Jimmy Ba, Volodymyr Mnih, Koray Kavukcuoglu
We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend…
Move Evaluation in Go Using Deep Convolutional Neural Networks
Chris J. Maddison, Aja Huang, Ilya Sutskever +1
The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep c…
Deep Structured Output Learning for Unconstrained Text Recognition
Max Jaderberg, Karen Simonyan, Andrea Vedaldi +1
We develop a representation suitable for the unconstrained recognition of words in natural images: the general case of no fixed lexicon and unknown length. To this end we propose a…
Recurrent Models of Visual Attention
Volodymyr Mnih, Nicolas Heess, Alex Graves +1
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a…
Distributed Parameter Estimation in Probabilistic Graphical Models
Yariv Dror Mizrahi, Misha Denil, Nando de Freitas
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composi…
Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed +1
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practi…