Jonathon Shlens

According to our database, Jonathon Shlens authored at least 69 papers between 2005 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

MOFI: Learning Image Representations from Noisy Entity Annotated Images.

Wentao Wu, Aleksei Timofeev, Chen Chen, Bowen Zhang, Kun Duan, Shuangning Liu, Yantao Zheng, Jonathon Shlens, Xianzhi Du, Zhe Gan, Yinfei Yang

CoRR, 2023

On Robustness in Multimodal Learning.

Brandon McKinzie, Joseph Yitan Cheng, Vaishaal Shankar, Yinfei Yang, Jonathon Shlens, Alexander Toshev

CoRR, 2023

STAIR: Learning Sparse Text and Image Representation in Grounded Tokens.

Chen Chen, Bowen Zhang, Liangliang Cao, Jiguang Shen, Tom Gunter, Albin Madappally Jose, Alexander Toshev, Jonathon Shlens, Ruoming Pang, Yinfei Yang

CoRR, 2023

Robustness in Multimodal Learning under Train-Test Modality Mismatch.

Brandon McKinzie, Vaishaal Shankar, Joseph Yitan Cheng, Yinfei Yang, Jonathon Shlens, Alexander T. Toshev

Proceedings of the International Conference on Machine Learning, 2023

Perceptual Grouping in Contrastive Vision-Language Models.

Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, Jonathon Shlens

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

STAIR: Learning Sparse Text and Image Representation in Grounded Tokens.

Chen Chen, Bowen Zhang, Liangliang Cao, Jiguang Shen, Tom Gunter, Albin Madappally Jose, Alexander Toshev, Yantao Zheng, Jonathon Shlens, Ruoming Pang, Yinfei Yang

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Scalable Scene Flow From Point Clouds in the Real World.

Philipp Jund, Chris Sweeney, Nichola Abdo, Zhifeng Chen, Jonathon Shlens

IEEE Robotics Autom. Lett., 2022

Perceptual Grouping in Vision-Language Models.

Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, Jonathon Shlens

CoRR, 2022

Scene Transformer: A unified architecture for predicting future trajectories of multiple agents.

Jiquan Ngiam, Vijay Vasudevan, Benjamin Caine, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David J. Weiss, Ben Sapp, Zhifeng Chen, Jonathon Shlens

Proceedings of the Tenth International Conference on Learning Representations, 2022

PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds.

Zhaoqi Leng, Shuyang Cheng, Benjamin Caine, Weiyue Wang, Xiao Zhang, Jonathon Shlens, Mingxing Tan, Dragomir Anguelov

Proceedings of the Computer Vision - ECCV 2022, 2022

Mitigating Bias in Calibration Error Estimation.

Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Scene Transformer: A unified multi-task model for behavior prediction and planning.

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

CoRR, 2021

Pseudo-labeling for Scalable 3D Object Detection.

Benjamin Caine, Rebecca Roelofs, Vijay Vasudevan, Jiquan Ngiam, Yuning Chai, Zhifeng Chen, Jonathon Shlens

CoRR, 2021

Soft Calibration Objectives for Neural Networks.

Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Revisiting ResNets: Improved Training and Scaling Strategies.

Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset.

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

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Scaling Local Self-Attention for Parameter Efficient Visual Backbones.

Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake A. Hechtman, Jonathon Shlens

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Bottleneck Transformers for Visual Recognition.

Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.

Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens

CoRR, 2020

Revisiting Spatial Invariance with Low-Rank Local Connectivity.

Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Data Augmentation Strategies for Object Detection.

Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le

Proceedings of the Computer Vision - ECCV 2020, 2020

Streaming Object Detection for 3-D Point Clouds.

Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen

Proceedings of the Computer Vision - ECCV 2020, 2020

Improving 3D Object Detection Through Progressive Population Based Augmentation.

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

Proceedings of the Computer Vision - ECCV 2020, 2020

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.

Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens

Proceedings of the Computer Vision - ECCV 2020, 2020

Scalability in Perception for Autonomous Driving: Waymo Open Dataset.

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

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Randaugment: Practical automated data augmentation with a reduced search space.

Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

RandAugment: Practical data augmentation with no separate search.

Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le

CoRR, 2019

StarNet: Targeted Computation for Object Detection in Point Clouds.

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

CoRR, 2019

Visual Wake Words Dataset.

Aakanksha Chowdhery, Pete Warden, Jonathon Shlens, Andrew Howard, Rocky Rhodes

CoRR, 2019

Using learned optimizers to make models robust to input noise.

Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk

CoRR, 2019

Using Videos to Evaluate Image Model Robustness.

Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc V. Le, Jonathon Shlens

CoRR, 2019

Accelerating Training of Deep Neural Networks with a Standardization Loss.

Jasmine Collins, Johannes Ballé, Jonathon Shlens

CoRR, 2019

A Fourier Perspective on Model Robustness in Computer Vision.

Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin Dogus Cubuk, Justin Gilmer

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stand-Alone Self-Attention in Vision Models.

Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Learned Representation for Scalable Vector Graphics.

Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Attention Augmented Convolutional Networks.

Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Do Better ImageNet Models Transfer Better?

Simon Kornblith, Jonathon Shlens, Quoc V. Le

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning a neural response metric for retinal prosthesis.

Nishal P. Shah, Sasidhar Madugula, E. J. Chichilnisky, Yoram Singer, Jonathon Shlens

Proceedings of the 6th International Conference on Learning Representations, 2018

A Dataset and Architecture for Visual Reasoning with a Working Memory.

Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo

Proceedings of the Computer Vision - ECCV 2018, 2018

Progressive Neural Architecture Search.

Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan L. Yuille, Jonathan Huang, Kevin Murphy

Proceedings of the Computer Vision - ECCV 2018, 2018

Learning Transferable Architectures for Scalable Image Recognition.

Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Recurrent Segmentation for Variable Computational Budgets.

Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

2017

Progressive Neural Architecture Search.

Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan L. Yuille, Jonathan Huang, Kevin Murphy

CoRR, 2017

Conditional Image Synthesis with Auxiliary Classifier GANs.

Augustus Odena, Christopher Olah, Jonathon Shlens

Proceedings of the 34th International Conference on Machine Learning, 2017

A Learned Representation For Artistic Style.

Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur

Proceedings of the 5th International Conference on Learning Representations, 2017

Pixel Recursive Super Resolution.

Ryan Dahl, Mohammad Norouzi, Jonathon Shlens

Proceedings of the IEEE International Conference on Computer Vision, 2017

YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video.

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

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

PixColor: Pixel Recursive Colorization.

Sergio Guadarrama, Ryan Dahl, David Bieber, Jonathon Shlens, Mohammad Norouzi, Kevin Murphy

Proceedings of the British Machine Vision Conference 2017, 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 British Machine Vision Conference 2017, 2017

2016

Net2Net: Accelerating Learning via Knowledge Transfer.

Tianqi Chen, Ian J. Goodfellow, Jonathon Shlens

Proceedings of the 4th International Conference on Learning Representations, 2016

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

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

CoRR, 2016

Rethinking the Inception Architecture for Computer Vision.

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Deep Networks With Large Output Spaces.

Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, Jay Yagnik

Proceedings of the 3rd International Conference on Learning Representations, 2015

Adversarial Autoencoders.

Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian J. Goodfellow

CoRR, 2015

Explaining and Harnessing Adversarial Examples.

Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy

Proceedings of the 3rd International Conference on Learning Representations, 2015

2014

A Tutorial on Independent Component Analysis.

Jonathon Shlens

CoRR, 2014

Notes on Kullback-Leibler Divergence and Likelihood.

Jonathon Shlens

CoRR, 2014

Notes on Generalized Linear Models of Neurons.

Jonathon Shlens

CoRR, 2014

A Light Discussion and Derivation of Entropy.

Jonathon Shlens

CoRR, 2014

A Tutorial on Principal Component Analysis.

Jonathon Shlens

CoRR, 2014

Zero-Shot Learning by Convex Combination of Semantic Embeddings.

Mohammad Norouzi, Tomás Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, Jeffrey Dean

Proceedings of the 2nd International Conference on Learning Representations, 2014

2013

Using Web Co-occurrence Statistics for Improving Image Categorization.

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

CoRR, 2013

DeViSE: A Deep Visual-Semantic Embedding Model.

Andrea Frome, Gregory S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'Aurelio Ranzato, Tomás Mikolov

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Fast, Accurate Detection of 100, 000 Object Classes on a Single Machine.

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

Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012

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 P. Simoncelli, Liam Paninski

J. Comput. Neurosci., 2012

Three Controversial Hypotheses Concerning Computation in the Primate Cortex.

Thomas L. Dean, Greg Corrado, Jonathon Shlens

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

2007

Estimating Information Rates with Confidence Intervals in Neural Spike Trains.

Jonathon Shlens, Matthew B. Kennel, Henry D. I. Abarbanel, E. J. Chichilnisky

Neural Comput., 2007

2005

Estimating Entropy Rates with Bayesian Confidence Intervals.

Matthew B. Kennel, Jonathon Shlens, Henry D. I. Abarbanel, E. J. Chichilnisky

Neural Comput., 2005