Yao Qin
Yao Qin
UCSB & Google DeepMind
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
Hierarchical Cellular Automata for Visual Saliency
Y Qin, M Feng, H Lu, GW Cottrell
International Journal of Computer Vision 126, 751-770, 2018
Are Vision Transformers Robust to Patch Perturbations?
J Gu, V Tresp, Y Qin
European Conference on Computer Vision (ECCV), 404-421, 2022
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
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
Deflecting Adversarial Attacks
Y Qin, N Frosst, C Raffel, G Cottrell, G Hinton
arXiv preprint arXiv:2002.07405, 2020
Training deep Boltzmann networks with sparse Ising machines
S Niazi, S Chowdhury, NA Aadit, M Mohseni, Y Qin, KY Camsari
Nature Electronics, 1-10, 2024
Improving uncertainty estimates through the relationship with adversarial robustness
Y Qin, X Wang, A Beutel, EH Chi
arXiv preprint arXiv:2006.16375, 2020
Evaluation Methodology for Attacks against Confidence Thresholding Models
I Goodfellow, Y Qin, D Berthelot
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
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
Improving classifier robustness through active generative counterfactual data augmentation
A Balashankar, X Wang, Y Qin, B Packer, N Thain, E Chi, J Chen, ...
Findings of the Association for Computational Linguistics: EMNLP 2023, 127-139, 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
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
X Zhang, S Li, X Yang, C Tian, Y Qin, LR Petzold
International Conference on Learning Representations (ICLR), 2024
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