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Ian Goodfellow
Researcher at Google
Publications - 139
Citations - 178656
Ian Goodfellow is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & MNIST database. The author has an hindex of 85, co-authored 137 publications receiving 135390 citations. Previous affiliations of Ian Goodfellow include OpenAI & Université de Montréal.
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Adversarial Examples that Fool both Human and Computer Vision
Gamaleldin F. Elsayed,Shreya Shankar,Brian Cheung,Nicolas Papernot,Alex Kurakin,Ian Goodfellow,Jascha Sohl-Dickstein +6 more
TL;DR: It is found that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.
Proceedings Article
An empirical analysis of dropout in piecewise linear networks
TL;DR: In this article, the authors empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function, and explore the effect of tied weights on the ensemble interpretation by training ensembles of masked networks without tied weights.
Posted Content
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
William Fedus,Mihaela Rosca,Balaji Lakshminarayanan,Andrew M. Dai,Shakir Mohamed,Ian Goodfellow +5 more
TL;DR: It is demonstrated that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail, and it is contributed to a growing body of evidence thatGAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.
Proceedings Article
Adversarial Reprogramming of Neural Networks
TL;DR: This paper demonstrates adversarial reprogramming on six ImageNet classification models, repurposing these models to perform a counting task, as well as classification tasks: classification of MNIST and CIFAR-10 examples presented as inputs to the ImageNet model.
Unsupervised and transfer learning challenge: a deep learning approach
Grégoire Mesnil,Yann N. Dauphin,Xavier Glorot,Salah Rifai,Yoshua Bengio,Ian Goodfellow,Erick Lavoie,Xavier Muller,Guillaume Desjardins,David Warde-Farley,Pascal Vincent,Aaron Courville,James Bergstra +12 more
TL;DR: This paper describes different kinds of layers the authors trained for learning representations in the setting of the Unsupervised and Transfer Learning Challenge, and the particular one-layer learning algorithms feeding a simple linear classifier with a tiny number of labeled training samples.