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Minds and Machines

Minds and Machines is a peer-reviewed academic journal covering artificial intelligence, philosophy, and cognitive science. The journal was… Expand
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Papers overview

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2018
2018
Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors… Expand
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Highly Cited
2017
Highly Cited
2017
3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible… Expand
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Highly Cited
2017
Highly Cited
2017
Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local… Expand
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Highly Cited
2017
Highly Cited
2017
We study the problem of learning generative models of 3D shapes. Voxels or 3D parts have been widely used as the underlying… Expand
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Highly Cited
2017
Highly Cited
2017
3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible… Expand
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Highly Cited
2016
Highly Cited
2016
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN… Expand
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Highly Cited
2016
Highly Cited
2016
While the universal approximation property holds both for hierarchical and shallow networks, we prove that deep (hierarchical… Expand
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Highly Cited
2015
Highly Cited
2015
In this paper, we consider the task of learning control policies for text-based games. In these games, all interactions in the… Expand
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Highly Cited
2014
Highly Cited
2014
Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image… Expand
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Highly Cited
2003
Highly Cited
2003
List of Figures.List of Tables.1. The Kids in the Hall.Synthetic Versus Analytic Traditions. .2. Advantages and Disadvantages of… Expand
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