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ORCIDJonathon Shlens
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- affiliation: Google Brain, Mountain View, CA, USA
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2020 – today
- 2022
[j4]Philipp Jund, Chris Sweeney
, Nichola Abdo, Zhifeng Chen, Jonathon Shlens:
Scalable Scene Flow From Point Clouds in the Real World. IEEE Robotics Autom. Lett. 7(2): 1589-1596 (2022)- 2021
- [c39]Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake A. Hechtman, Jonathon Shlens:
Scaling Local Self-Attention for Parameter Efficient Visual Backbones. CVPR 2021: 12894-12904 - [c38]Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani:
Bottleneck Transformers for Visual Recognition. CVPR 2021: 16519-16529 - [c37]Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles R. Qi, Yin Zhou, Zoey Yang, Aurelien Chouard, Pei Sun, Jiquan Ngiam, Vijay Vasudevan, Alexander McCauley, Jonathon Shlens, Dragomir Anguelov:
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset. ICCV 2021: 9690-9699 - [c36]Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph:
Revisiting ResNets: Improved Training and Scaling Strategies. NeurIPS 2021: 22614-22627 - [c35]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. NeurIPS 2021: 29768-29779 - [i46]Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani:
Bottleneck Transformers for Visual Recognition. CoRR abs/2101.11605 (2021) - [i45]Philipp Jund, Chris Sweeney, Nichola Abdo, Zhifeng Chen, Jonathon Shlens:
Scalable Scene Flow from Point Clouds in the Real World. CoRR abs/2103.01306 (2021) - [i44]Benjamin Caine, Rebecca Roelofs, Vijay Vasudevan, Jiquan Ngiam, Yuning Chai, Zhifeng Chen, Jonathon Shlens:
Pseudo-labeling for Scalable 3D Object Detection. CoRR abs/2103.02093 (2021) - [i43]Irwan Bello, William Fedus, Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph:
Revisiting ResNets: Improved Training and Scaling Strategies. CoRR abs/2103.07579 (2021) - [i42]Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake A. Hechtman, Jonathon Shlens:
Scaling Local Self-Attention for Parameter Efficient Visual Backbones. CoRR abs/2103.12731 (2021) - [i41]Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Benjamin Sapp, Charles R. Qi, Yin Zhou, Zoey Yang, Aurelien Chouard, Pei Sun, Jiquan Ngiam, Vijay Vasudevan, Alexander McCauley, Jonathon Shlens, Dragomir Anguelov:
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset. CoRR abs/2104.10133 (2021) - [i40]Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David Weiss, Benjamin Sapp, Zhifeng Chen, Jonathon Shlens:
Scene Transformer: A unified multi-task model for behavior prediction and planning. CoRR abs/2106.08417 (2021) - [i39]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. CoRR abs/2108.00106 (2021) - 2020
- [c34]Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, Dragomir Anguelov:
Scalability in Perception for Autonomous Driving: Waymo Open Dataset. CVPR 2020: 2443-2451 - [c33]Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le:
Randaugment: Practical automated data augmentation with a reduced search space. CVPR Workshops 2020: 3008-3017 - [c32]Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov:
Improving 3D Object Detection Through Progressive Population Based Augmentation. ECCV (21) 2020: 279-294 - [c31]Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen:
Streaming Object Detection for 3-D Point Clouds. ECCV (18) 2020: 423-441 - [c30]Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le:
Learning Data Augmentation Strategies for Object Detection. ECCV (27) 2020: 566-583 - [c29]Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens:
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation. ECCV (9) 2020: 695-714 - [c28]Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith:
Revisiting Spatial Invariance with Low-Rank Local Connectivity. ICML 2020: 2868-2879 - [c27]Ekin Dogus Cubuk, Barret Zoph, Jonathon Shlens, Quoc Le:
RandAugment: Practical Automated Data Augmentation with a Reduced Search Space. NeurIPS 2020 - [i38]Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith:
Revisiting Spatial Invariance with Low-Rank Local Connectivity. CoRR abs/2002.02959 (2020) - [i37]Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov:
Improving 3D Object Detection through Progressive Population Based Augmentation. CoRR abs/2004.00831 (2020) - [i36]Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen:
Streaming Object Detection for 3-D Point Clouds. CoRR abs/2005.01864 (2020) - [i35]Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens:
Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation. CoRR abs/2005.10266 (2020) - [i34]Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer:
Mitigating bias in calibration error estimation. CoRR abs/2012.08668 (2020)
2010 – 2019
- 2019
- [c26]Simon Kornblith, Jonathon Shlens, Quoc V. Le:
Do Better ImageNet Models Transfer Better? CVPR 2019: 2661-2671 - [c25]Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens:
Attention Augmented Convolutional Networks. ICCV 2019: 3285-3294 - [c24]Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens:
A Learned Representation for Scalable Vector Graphics. ICCV 2019: 7929-7938 - [c23]Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens:
A Learned Representation for Scalable Vector Graphics. DGS@ICLR 2019 - [c22]Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens:
Stand-Alone Self-Attention in Vision Models. NeurIPS 2019: 68-80 - [c21]Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin Dogus Cubuk, Justin Gilmer:
A Fourier Perspective on Model Robustness in Computer Vision. NeurIPS 2019: 13255-13265 - [i33]Jasmine Collins, Johannes Ballé, Jonathon Shlens:
Accelerating Training of Deep Neural Networks with a Standardization Loss. CoRR abs/1903.00925 (2019) - [i32]Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens:
A Learned Representation for Scalable Vector Graphics. CoRR abs/1904.02632 (2019) - [i31]Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, Quoc V. Le:
Attention Augmented Convolutional Networks. CoRR abs/1904.09925 (2019) - [i30]Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc V. Le, Jonathon Shlens:
Using Videos to Evaluate Image Model Robustness. CoRR abs/1904.10076 (2019) - [i29]Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk:
Using learned optimizers to make models robust to input noise. CoRR abs/1906.03367 (2019) - [i28]Aakanksha Chowdhery, Pete Warden, Jonathon Shlens, Andrew Howard, Rocky Rhodes:
Visual Wake Words Dataset. CoRR abs/1906.05721 (2019) - [i27]Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens:
Stand-Alone Self-Attention in Vision Models. CoRR abs/1906.05909 (2019) - [i26]Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer:
A Fourier Perspective on Model Robustness in Computer Vision. CoRR abs/1906.08988 (2019) - [i25]Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le:
Learning Data Augmentation Strategies for Object Detection. CoRR abs/1906.11172 (2019) - [i24]Jiquan Ngiam, Benjamin Caine, Wei Han, Brandon Yang, Yuning Chai, Pei Sun, Yin Zhou, Xi Yi, Ouais Alsharif, Patrick Nguyen, Zhifeng Chen, Jonathon Shlens, Vijay Vasudevan:
StarNet: Targeted Computation for Object Detection in Point Clouds. CoRR abs/1908.11069 (2019) - [i23]Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le:
RandAugment: Practical data augmentation with no separate search. CoRR abs/1909.13719 (2019) - [i22]Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, Dragomir Anguelov:
Scalability in Perception for Autonomous Driving: Waymo Open Dataset. CoRR abs/1912.04838 (2019) - 2018
- [c20]Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens:
Recurrent Segmentation for Variable Computational Budgets. CVPR Workshops 2018: 1648-1657 - [c19]Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le:
Learning Transferable Architectures for Scalable Image Recognition. CVPR 2018: 8697-8710 - [c18]Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan L. Yuille, Jonathan Huang, Kevin Murphy:
Progressive Neural Architecture Search. ECCV (1) 2018: 19-35 - [c17]Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo:
A Dataset and Architecture for Visual Reasoning with a Working Memory. ECCV (10) 2018: 729-745 - [c16]Nishal P. Shah, Sasidhar Madugula, E. J. Chichilnisky, Yoram Singer, Jonathon Shlens:
Learning a neural response metric for retinal prosthesis. ICLR (Poster) 2018 - [c15]Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens:
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction. NeurIPS 2018: 8713-8724 - [i21]Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo:
A dataset and architecture for visual reasoning with a working memory. CoRR abs/1803.06092 (2018) - [i20]Simon Kornblith, Jonathon Shlens, Quoc V. Le:
Do Better ImageNet Models Transfer Better? CoRR abs/1805.08974 (2018) - [i19]Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens:
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction. CoRR abs/1809.04184 (2018) - 2017
- [c14]Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens:
Exploring the structure of a real-time, arbitrary neural artistic stylization network. BMVC 2017 - [c13]Sergio Guadarrama, Ryan Dahl, David Bieber, Jonathon Shlens, Mohammad Norouzi, Kevin Murphy:
PixColor: Pixel Recursive Colorization. BMVC 2017 - [c12]Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, Vincent Vanhoucke:
YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video. CVPR 2017: 7464-7473 - [c11]Ryan Dahl, Mohammad Norouzi, Jonathon Shlens:
Pixel Recursive Super Resolution. ICCV 2017: 5449-5458 - [c10]Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur:
A Learned Representation For Artistic Style. ICLR (Poster) 2017 - [c9]Augustus Odena, Christopher Olah, Jonathon Shlens:
Conditional Image Synthesis with Auxiliary Classifier GANs. ICML 2017: 2642-2651 - [i18]Ryan Dahl, Mohammad Norouzi, Jonathon Shlens:
Pixel Recursive Super Resolution. CoRR abs/1702.00783 (2017) - [i17]Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, Vincent Vanhoucke:
YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video. CoRR abs/1702.00824 (2017) - [i16]Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens:
Exploring the structure of a real-time, arbitrary neural artistic stylization network. CoRR abs/1705.06830 (2017) - [i15]Sergio Guadarrama, Ryan Dahl, David Bieber, Mohammad Norouzi, Jonathon Shlens, Kevin Murphy:
PixColor: Pixel Recursive Colorization. CoRR abs/1705.07208 (2017) - [i14]Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le:
Learning Transferable Architectures for Scalable Image Recognition. CoRR abs/1707.07012 (2017) - [i13]Lane McIntosh, David Sussillo, Niru Maheswaranathan, Jonathon Shlens:
Recurrent Segmentation for Variable Computational Budgets. CoRR abs/1711.10151 (2017) - [i12]Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan L. Yuille, Jonathan Huang, Kevin Murphy:
Progressive Neural Architecture Search. CoRR abs/1712.00559 (2017) - 2016
- [c8]Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna:
Rethinking the Inception Architecture for Computer Vision. CVPR 2016: 2818-2826 - [c7]Tianqi Chen, Ian J. Goodfellow, Jonathon Shlens:
Net2Net: Accelerating Learning via Knowledge Transfer. ICLR 2016 - [i11]Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR abs/1603.04467 (2016) - [i10]Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur:
A Learned Representation For Artistic Style. CoRR abs/1610.07629 (2016) - [i9]Augustus Odena, Christopher Olah, Jonathon Shlens:
Conditional Image Synthesis With Auxiliary Classifier GANs. CoRR abs/1610.09585 (2016) - 2015
- [c6]Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy:
Explaining and Harnessing Adversarial Examples. ICLR (Poster) 2015 - [c5]Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, Jay Yagnik:
Deep Networks With Large Output Spaces. ICLR (Workshop) 2015 - [i8]Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian J. Goodfellow:
Adversarial Autoencoders. CoRR abs/1511.05644 (2015) - [i7]Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna:
Rethinking the Inception Architecture for Computer Vision. CoRR abs/1512.00567 (2015) - 2014
- [c4]Mohammad Norouzi, Tomás Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, Jeffrey Dean:
Zero-Shot Learning by Convex Combination of Semantic Embeddings. ICLR 2014 - [i6]Jonathon Shlens:
A Tutorial on Principal Component Analysis. CoRR abs/1404.1100 (2014) - [i5]Jonathon Shlens:
A Light Discussion and Derivation of Entropy. CoRR abs/1404.1998 (2014) - [i4]Jonathon Shlens:
Notes on Generalized Linear Models of Neurons. CoRR abs/1404.1999 (2014) - [i3]Jonathon Shlens:
Notes on Kullback-Leibler Divergence and Likelihood. CoRR abs/1404.2000 (2014) - [i2]Jonathon Shlens:
A Tutorial on Independent Component Analysis. CoRR abs/1404.2986 (2014) - 2013
- [c3]Thomas L. Dean, Mark A. Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik:
Fast, Accurate Detection of 100, 000 Object Classes on a Single Machine. CVPR 2013: 1814-1821 - [c2]Andrea Frome, Gregory S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'Aurelio Ranzato, Tomás Mikolov:
DeViSE: A Deep Visual-Semantic Embedding Model. NIPS 2013: 2121-2129 - [i1]Samy Bengio, Jeffrey Dean, Dumitru Erhan, Eugene Ie, Quoc V. Le, Andrew Rabinovich, Jonathon Shlens, Yoram Singer:
Using Web Co-occurrence Statistics for Improving Image Categorization. CoRR abs/1312.5697 (2013) - 2012
- [j3]Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W. Pillow, Jayant Kulkarni, Alan M. Litke, E. J. Chichilnisky, Eero P. Simoncelli, Liam Paninski:
Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J. Comput. Neurosci. 33(1): 97-121 (2012) - [c1]Thomas L. Dean, Greg Corrado, Jonathon Shlens:
Three Controversial Hypotheses Concerning Computation in the Primate Cortex. AAAI 2012
2000 – 2009
- 2007
- [j2]Jonathon Shlens, Matthew B. Kennel, Henry D. I. Abarbanel, E. J. Chichilnisky:
Estimating Information Rates with Confidence Intervals in Neural Spike Trains. Neural Comput. 19(7): 1683-1719 (2007) - 2005
- [j1]Matthew B. Kennel, Jonathon Shlens, Henry D. I. Abarbanel, E. J. Chichilnisky:
Estimating Entropy Rates with Bayesian Confidence Intervals. Neural Comput. 17(7): 1531-1576 (2005)
Coauthor Index
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