8.1k citations · 11.3k across the 9 of their papers we have counts for
9 papers
Accelerating Training of Deep Neural Networks with a Standardization Loss
Jasmine Collins, Johannes Balle, Jonathon Shlens
A significant advance in accelerating neural network training has been the development of normalization methods, permitting the training of deep models both faster and with better…
A Learned Representation For Artistic Style
Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur
The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual voca…
Deep Networks With Large Output Spaces
Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga +1
Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, th…
Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbatio…
A Tutorial on Independent Component Analysis
Jonathon Shlens
Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning. This tutorial provides…
Notes on Kullback-Leibler Divergence and Likelihood
Jonathon Shlens
The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies the proximity of two probability distributions. Although difficult to understan…