| 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 | 1685 | 2017 |
| Saliency Detection via Cellular Automata Y Qin, H Lu, Y Xu, H Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 110-119, 2015 | 643 | 2015 |
| Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition Y Qin, N Carlini, G Cottrell, I Goodfellow, C Raffel International Conference on Machine Learning (ICML), 5231-5240, 2019 | 507 | 2019 |
| 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 | 148 | 2023 |
| Autofocus Layer for Semantic Segmentation Y Qin, K Kamnitsas, S Ancha, J Nanavati, G Cottrell, A Criminisi, A Nori Medical Image Computing and Computer Assisted Intervention (MICCAI), 603-611, 2018 | 143 | 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 | 103 | 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 | 96 | 2020 |
| Are Vision Transformers Robust to Patch Perturbations? J Gu, V Tresp, Y Qin European Conference on Computer Vision (ECCV), 404-421, 2022 | 73 | 2022 |
| Hierarchical Cellular Automata for Visual Saliency Y Qin, M Feng, H Lu, GW Cottrell International Journal of Computer Vision 126, 751-770, 2018 | 73 | 2018 |
| Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation Y Qin, C Zhang, T Chen, B Lakshminarayanan, A Beutel, X Wang Advances in Neural Information Processing Systems 35, 16276-16289, 2022 | 50 | 2022 |
| Improving Calibration through the Relationship with Adversarial Robustness Y Qin, X Wang, A Beutel, E Chi Advances in Neural Information Processing Systems 34, 14358-14369, 2021 | 28 | 2021 |
| Training deep Boltzmann networks with sparse Ising machines S Niazi, S Chowdhury, NA Aadit, M Mohseni, Y Qin, KY Camsari Nature Electronics 7 (7), 610-619, 2024 | 27 | 2024 |
| Deflecting Adversarial Attacks Y Qin, N Frosst, C Raffel, G Cottrell, G Hinton arXiv preprint arXiv:2002.07405, 2020 | 23 | 2020 |
| Improving uncertainty estimates through the relationship with adversarial robustness Y Qin, X Wang, A Beutel, EH Chi arXiv preprint arXiv:2006.16375, 2020 | 12 | 2020 |
| 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 Advances in Neural Information Processing Systems, 2023 | 11 | 2023 |
| Fast Decision Boundary based Out-of-Distribution Detector L Liu, Y Qin International Conference on Machine Learning (ICML), 2024 | 8 | 2024 |
| Evaluation Methodology for Attacks against Confidence Thresholding Models I Goodfellow, Y Qin, D Berthelot | 8 | 2018 |
| Initialization Matters for Adversarial Transfer Learning A Hua, J Gu, Z Xue, N Carlini, E Wong, Y Qin IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 | 7 | 2024 |
| 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 | 6 | 2023 |
| What are effective labels for augmented data? improving calibration and robustness with autolabel Y Qin, X Wang, B Lakshminarayanan, EH Chi, A Beutel 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 365-376, 2023 | 6 | 2023 |
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