{{link.text}}

{{link.text}}

Jonathon Shlens

Jon Shlens is a senior research scientist at Google since 2010. Prior to joining Google Research he was a research fellow at the Howard Hughes Medical Institute and a Miller Fellow at UC Berkeley. His research interests include machine perception, statistical signal processing, machine learning and biological neuroscience.

Google Publications

Towards learning a metric for neural prosthetics

Nishal P Shah, Sasidhar Madugula, Alan Litke, Alexander Sher, EJ Chichilnisky, Yoram Singer, Jonathon Shlens

ICLR (2018) (to appear)
A Learned Representation For Artistic Style

Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur

ICLR (2017)
Conditional Image Synthesis With Auxiliary Classifier GANs

Augustus Odena, Christopher Olah, Jonathon Shlens

ICML (2017)
Exploring the structure of a real-time, arbitrary neural artistic stylization network

Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens

Proceedings of the 28th British Machine Vision Conference (BMVC) (2017)
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) (2017)
Pixel Recursive Super Resolution

Ryan Dahl, Mohammad Norouzi, Jonathan Shlens

ICCV (2017)
YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Dataset for Object Detection in Video

Esteban Real, Jon Shlens, Stefano Mazzocchi, Vincent Vanhoucke, Xin Pan

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7464-7473
Adversarial Autoencoders

Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow

International Conference on Learning Representations (2016)
Net2Net: Accelerating Learning via Knowledge Transfer

Tianqi Chen, Ian Goodfellow, Jonathon Shlens

International Conference on Learning Representations (2016)
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, (2016)
Deep Networks With Large Output Spaces

Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, Jay Yagnik

International Conference on Learning Representations (2015)
Explaining and Harnessing Adversarial Examples

Ian Goodfellow, Jonathon Shlens, Christian Szegedy

International Conference on Learning Representations (2015)
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg 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 Mané, 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 Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

tensorflow.org (2015)
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 (2014)
DeViSE: A Deep Visual-Semantic Embedding Model

Andrea Frome, Greg Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, Tomas Mikolov

Neural Information Processing Systems (NIPS) (2013)
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine

Thomas Dean, Mark Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC, USA (2013)
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine: Technical Supplement

Thomas Dean, Mark Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC, USA (2013)
Using Web Co-occurrence Statistics for Improving Image Categorization

Samy Bengio, Jeffrey Dean, Dumitru Erhan, Eugene Ie, Quoc Le, Andrew Rabinovich, Jonathon Shlens, Yoram Singer

arXiv (2013)
Three Controversial Hypotheses Concerning Computation in the Primate Cortex

Thomas Dean, Greg Corrado, Jonathon Shlens

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI Press (2012)