Shlens, Jonathon

Explaining and Harnessing Adversarial Examples

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 10,381

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv

Parallel Computing and Optimization TechniquesCloud Computing and Resource Management

Cited by 9,408

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Autonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

Cited by 3,437

RandAugment: Practical automated data augmentation with a reduced search space

Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2019) · arXiv

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 3,223

Randaugment: Practical automated data augmentation with a reduced search space

Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 3,223

Adversarial Autoencoders

Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 1,579

Bottleneck Transformers for Visual Recognition

Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 1,385

Do Better ImageNet Models Transfer Better?

Kornblith, S., Shlens, J., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,325

Stand-Alone Self-Attention in Vision Models

Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., & Shlens, J. (2019) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 977

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2015) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 741

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

Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C., Zhou, Y., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Autonomous Vehicle Technology and SafetyTraffic Prediction and Management Techniques

Cited by 584

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

Chen, L.-C., Collins, M. D., Zhu, Y., Papandreou, G., Zoph, B., Schroff, F., Adam, H., & Shlens, J. (2018) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 347

Exploring the structure of a real-time, arbitrary neural artistic stylization network

Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., & Shlens, J. (2017) · Procedings of the British Machine Vision Conference 2017

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 306

Progressive Neural Architecture Search

Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., & Murphy, K. (2017) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 171

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

Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J. (2020) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 165

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

Scene Transformer: A unified architecture for predicting multiple agent trajectories

Ngiam, J., Caine, B., Vasudevan, V., Zhang, Z., Chiang, H.-T. L., Ling, J., Roelofs, R., Bewley, A., Liu, C., Venugopal, A., et al. (2021) · arXiv

Autonomous Vehicle Technology and SafetyTraffic Prediction and Management Techniques

Cited by 84

MedGemma Technical Report

Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 49

On robustness in multimodal learning

McKinzie, B., Cheng, J., Shankar, V., Yang, Y., Shlens, J., & Toshev, A. (2023) · arXiv

Multimodal Machine Learning ApplicationsAdversarial Robustness in Machine Learning

Cited by 1