| A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction Y Qin, D Song, H Chen, W Cheng, G Jiang, G Cottrell International Joint Conference on Artificial Intelligence (IJCAI), 2017, 2017 | 1224 | 2017 |
| Saliency Detection via Cellular Automata Y Qin, H Lu, Y Xu, H Wang Computer Vision and Pattern Recognition (CVPR), 2015, 110-119, 2015 | 635 | 2015 |
| Imperceptible, Robust and Targeted Adversarial Examples for Automatic Speech Recognition Y Qin, N Carlini, I Goodfellow, G Cottrell, C Raffel International Conference on Machine Learning (ICML), 2019., 2019 | 403 | 2019 |
| Autofocus Layer for Semantic Segmentation Y Qin, K Kamnitsas, S Ancha, J Nanavati, G Cottrell, A Criminisi, A Nori International Conference on Medical Image Computing and Computer Assisted …, 2018 | 122 | 2018 |
| Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions Y Qin*, N Frosst*, S Sabour, C Raffel, G Cottrell, G Hinton International Conference on Learning Representations (ICLR), 2020, 2020 | 83 | 2020 |
| CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation T Wang, X Wang, Y Qin, B Packer, K Li, J Chen, A Beutel, E Chi EMNLP 2020, 2020 | 67 | 2020 |
| Hierarchical Cellular Automata for Visual Saliency Y Qin, M Feng, H Lu, GW Cottrell International Journal of Computer Vision (IJCV), 2018, 2018 | 60 | 2018 |
| Are Vision Transformers Robust to Patch Perturbations? J Gu, V Tresp, Y Qin European Conference on Computer Vision (ECCV), 2022, 2022 | 39 | 2022 |
| Improving Calibration through the Relationship with Adversarial Robustness Y Qin, X Wang, A Beutel, EH Chi Neural Information Processing Systems (NeurIPS), 2021, 2021 | 30* | 2021 |
| Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation Y Qin, C Zhang, T Chen, B Lakshminarayanan, A Beutel, X Wang Neural Information Processing Systems (NeurIPS), 2022, 2022 | 29 | 2022 |
| Deflecting Adversarial Attacks Y Qin, N Frosst, C Raffel, G Cottrell, G Hinton | 19 | 2020 |
| A systematic survey of prompt engineering on vision-language foundation models J Gu, Z Han, S Chen, A Beirami, B He, G Zhang, R Liao, Y Qin, V Tresp, ... arXiv preprint arXiv:2307.12980, 2023 | 8 | 2023 |
| Evaluation Methodology for Attacks against Confidence Thresholding Models I Goodfellow, Y Qin, D Berthelot | 7 | 2018 |
| Opinion Evolution in Open Community Q Pan, Y Qin, Y Xu, M Tong, M He International Journal of Modern Physics C 28 (01), 1750003, 2017 | 6 | 2017 |
| Towards Robust Prompts on Vision-Language Models J Gu, A Beirami, X Wang, A Beutel, P Torr, Y Qin arXiv preprint arXiv:2304.08479, 2023 | 5 | 2023 |
| Training Deep Boltzmann Networks with Sparse Ising Machines S Niazi, NA Aadit, M Mohseni, S Chowdhury, Y Qin, KY Camsari arXiv preprint arXiv:2303.10728, 2023 | 5 | 2023 |
| Effective Robustness against Natural Distribution Shifts for Models with Different Training Data Z Shi, N Carlini, A Balashankar, L Schmidt, CJ Hsieh, A Beutel, Y Qin Neural Information Processing Systems (NeurIPS), 2023, 2023 | 3 | 2023 |
| What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel Y Qin, X Wang, B Lakshminarayanan, EH Chi, A Beutel IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023 | 2 | 2023 |
| Dual stage attention based recurrent neural network for time series prediction D Song, H Chen, G Jiang, Y Qin US Patent 10,929,674, 2021 | 2 | 2021 |
| Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting X Zhang, S Li, X Yang, C Tian, Y Qin, LR Petzold arXiv preprint arXiv:2305.12723, 2023 | 1 | 2023 |
Garrison W. CottrellProfessor of Computer Science and Engineering, University of California, San DiegoVerified email at ucsd.edu
