Jon Shlens

I am a staff scientist at Google Brain. I am broadly interested in the topics of vision and learning including:

  • Development of new methods of image processing, computer vision and machine learning.
  • Building an understanding how vision works in artificial and biological systems.
I am a co-inventor of TensorFlow developing many widely used vision systems. To learn more about my work, please explore my publications or tutorials.

Publications

2018

Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, Quoc V. Le
Under review

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Liang-Chieh Chen, Maxwell D.Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens
Neural Information Processing Systems (NIPS)

Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy
European Conference on Computer Vision (ECCV). Oral presentation

A Dataset and Architecture for Visual Reasoning with a Working Memory
Guangyu Robert Yang, Igor Ganichev, Xiao-jing Wang, Jonathon Shlens, David Sussillo
European Conference on Computer Vision (ECCV)

Recurrent Segmentation for Variable Computational Budgets
Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens
Workshop on Efficient Deep Learning for Computer Vision at CVPR

Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Spotlight presentation

Learning a Neural Response Metric for Retinal Prosthesis
Nishal P Shah, Sasidhar Madugula, EJ Chichilnisky, Yoram Singer, Jonathon Shlens
International Conference on Learning Representations (ICLR). Conference track.

2017

Exploring the structure of a real-time, arbitrary neural artistic stylization network
Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin and Jonathon Shlens
Proceedings of the 28th British Machine Vision Conference (BMVC). Oral presentation.

PixColor: Pixel recursive colorization
Sergio Guadarrama, Ryan Dahl, David Bieber, Mohammad Norouzi, Jonathon Shlens, Kevin Murphy
Proceedings of the 28th British Machine Vision Conference (BMVC). Oral presentation.

Pixel recursive super resolution
Ryan Dahl, Mohammad Norouzi, Jonathon Shlens
Proceedings of the IEEE International Conference on Computer Vision (ICCV).

Conditional Image Synthesis With Auxiliary Classifier GANs.
Augustus Odena, Christopher Olah and Jonathon Shlens
Proceedings of the 34th International Conference of Machine Learning (ICML)

YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan and Vincent Vanhoucke
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

A Learned Representation for Artistic Style
Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur
International Conference on Learning Representations (ICLR). Conference track.

2016

Net2Net: Accelerating Learning via Knowledge Transfer
Tianqi Chen, Ian Goodfellow, Jonathon Shlens
International Conference on Learning Representations (ICLR). Oral presentation.

Adversarial Autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow
International Conference on Learning Representations (ICLR). Workshop track.

Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

2015

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng
Unreviewed technical report.

Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, Christian Szegedy
International Conference on Learning Representations (ICLR). Conference track.

Deep Networks With Large Output Spaces
Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, Jay Yagnik
International Conference on Learning Representations (ICLR). Workshop track.

2014

Zero-Shot Learning by Convex Combination of Semantic Embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, Jeffrey Dean
International Conference on Learning Representations (ICLR). Oral presentation

2013

DeViSE: A Deep Visual-Semantic Embedding Model
Andrea Frome*, Greg Corrado*, Jonathon Shlens*, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato and Tomas Mikolov
Neural Information Processing Systems (NIPS)
* Equal contribution of authors.

Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
Thomas Dean, Mark Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Best Paper Award.

Three controversial hypotheses concerning computation in the primate cortex
Thomas Dean, Greg Corrado and Jonathon Shlens
Association for the Advancement of Artificial Intelligence (AAAI).

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.
Jonathan W. Pillow*, Jonathon Shlens*, EJ Chichilnisky and Eero Simoncelli
Public Library of Science (PLoS) One 8: 5
* Equal contribution of authors.

2012

Efficient Coding of Spatial Information in the Primate Retina
Eizaburo Doi, Jeffrey L. Gauthier, Greg D. Field, Jonathon Shlens, Alexander Sher, Martin Greschner, Timothy A. Machado, Lauren H. Jepson, Keith Mathieson, Deborah E. Gunning, Alan M. Litke, Liam Paninski, E. J. Chichilnisky and Eero P. Simoncelli
Journal of Neuroscience. 32:46

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells.
Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W. Pillow, Jayant Kulkarni, Alan M. Litke, E. J. Chichilnisky, Eero Simoncelli, Liam Paninski
Journal of Computational Neuroscience. 33:1

2011

Correlated firing among major ganglion cell types in primate retina
Martin Greschner, Jonathon Shlens, Tina Bakolitsa, Greg Field , Jeff Gauthier, Lauren Jepson, Alexander Sher, Alan Litke and E.J. Chichilnisky
Journal of Physiology

2010

Functional connectivity in the retina at the resolution of photoreceptors
Greg Field , Jeff Gauthier, Alexander Sher, Martin Greschner, Timothy Machado, Lauren Jepson, Jonathon Shlens, Deborah Gunning, Keith Mathieson, Wladyslaw Dabrowski, Liam Paninski, Alan Litke and E.J. Chichilnisky
Nature. 467, 673-677
supplement, news & views

2009

The structure of large-scale synchronized firing in primate retina
Jonathon Shlens, Greg Field , Jeff Gauthier, Martin Greschner, Alexander Sher, Alan Litke and E.J. Chichilnisky
Journal of Neuroscience. 29, 5022-5031

High sensitivity rod photoreceptor input to blue-yellow color opponent pathway in primate retina
Greg Field, Martin Greschner, Jeffrey Gauthier, Jonathon Shlens, Alexander Sher, Alan Litke and E.J. Chichilnisky
Nature Neuroscience. 12, 1150

Receptive fields in primate retina are coordinated to sample visual space more uniformly
Jeff Gauthier, Greg Field , Alexander Sher, Martin Greschner, Jonathon Shlens, Alan Litke and E.J. Chichilnisky
Public Library of Science (PLoS)) Biology. 7, e63.

Uniform signal redundancy of parasol and midget ganglion cells in primate retina
Jeff Gauthier, Greg Field , Alexander Sher, Jonathon Shlens, Martin Greschner, Alan Litke and E.J. Chichilnisky
Journal of Neuroscience. 29, 4675-4680.

2008

Synchronized firing in the retina
Jonathon Shlens, Fred Rieke, E.J. Chichilnisky
Current Opinions in Neurobiology. 16, 396-402.

Spatiotemporal correlations and visual signaling in a complete neuronal population
Jonathan Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan Litke, E.J. Chichilnisky and Eero Simoncelli
Nature. 454:995-999.

2007

Estimating information rates in neural spike trains with confidence intervals
Jonathon Shlens, Matthew Kennel, Henry Abarbanel and E.J. Chichilnisky
Neural Computation. 19, 1683-1719.

Spatial properties and functional organization of small bistratified cells in primate retina
Greg Field, Alexander Sher, Jeff Gauthier, Martin Greschner, Jonathon Shlens, Alan Litke and E.J. Chichilnisky
Journal of Neuroscience. 270, 13261

Identification and characterization of a Y-like primate retinal ganglion cell type
Dumitru Petrusca, Matthew Grivich, Alexander Sher, Greg Field, Jeff Gauthier, Jonathon Shlens, E.J. Chichilnisky and Alan Litke
Journal of Neuroscience. 27, 11019.

2006

The structure of multi-neuron firing patterns in primate retina
Jonathon Shlens, Greg Field, Jeff Gauthier, Matthew Grivich, Dumitru Petrusca, Alexander Sher, Alan Litke and E.J. Chichilnisky
Journal of Neuroscience. 26, 8254-8266.
cover, commentary

2005

Estimating entropy rates with Bayesian confidence intervals
Matthew Kennel, Jonathon Shlens, Henry Abarbanel and E.J. Chichilnisky
Neural Computation. 17, 1531-1576

Tutorials

These tutorials provide a general introduction to topics I find quite interesting but often lack good explanations in textbooks or the online literature.


Tutorial on Independent Component Analysis
A complete introduction and discussion of independent component analysis. Builds on previous tutorial on principal component analysis.
Version 1.0

Tutorial on Principal Component Analysis
A full introduction, description, derivation, and discussion of principal component analysis. Concrete examples for intuition building, the mathematical relation to SVD, and new extensions of this algorithm.
Version 3.02

A Light Discussion and Derivation of Entropy
A light discussion of the underlying assumptions behind entropy followed by a rigorous but simple derivation of the formula for entropy.
Version 1.01

Notes on Kullback-Leibler Divergence and Likelihood
An intuitive discussion about where Kullback-Leibler divergence arises and its relationship to likelihood theory.
Version 1.01

Notes on Generalized Linear Models of Neurons
An introduction to the application of GLMs to model neurons and networks of neurons. Brief discussion and derivation of primary equations pertaining to maximum likelihood estimation.
Version 1.51