| OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization N Ye, K Li, H Bai, R Yu, L Hong, F Zhou, Z Li, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 74 | 2022 |
| OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms N Ye, K Li, L Hong, H Bai, Y Chen, F Zhou, Z Li arXiv preprint arXiv:2106.03721, 2021 | 58 | 2021 |
| Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation H Bai, R Sun, L Hong, F Zhou, N Ye, HJ Ye, SHG Chan, Z Li Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6705-6713, 2021 | 58 | 2021 |
| Bayesian adversarial learning N Ye, Z Zhu Advances in Neural Information Processing Systems 31, 2018 | 58 | 2018 |
| Dataset and metrics for predicting local visible differences K Wolski, D Giunchi, N Ye, P Didyk, K Myszkowski, R Mantiuk, HP Seidel, ... ACM Transactions on Graphics (TOG) 37 (5), 1-14, 2018 | 42 | 2018 |
| NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization H Bai, F Zhou, L Hong, N Ye, SHG Chan, Z Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 40 | 2021 |
| Langevin dynamics with continuous tempering for training deep neural networks N Ye, Z Zhu, RK Mantiuk arXiv preprint arXiv:1703.04379, 2017 | 23 | 2017 |
| Predicting visible image differences under varying display brightness and viewing distance N Ye, K Wolski, RK Mantiuk Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 19 | 2019 |
| Stochastic fractional Hamiltonian Monte Carlo N Ye, Z Zhu Proceedings of the 27th International Joint Conference on Artificial …, 2018 | 17 | 2018 |
| Amata: An annealing mechanism for adversarial training acceleration N Ye, Q Li, XY Zhou, Z Zhu Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10691 …, 2021 | 16 | 2021 |
| Achieving adversarial robustness via sparsity N Liao, S Wang, L Xiang, N Ye, S Shao, P Chu Machine Learning, 1-27, 2022 | 14 | 2022 |
| An Annealing Mechanism for Adversarial Training Acceleration N Ye, Q Li, XY Zhou, Z Zhu IEEE Transactions on Neural Networks and Learning Systems, 2021 | 13 | 2021 |
| Visibility Metric for Visually Lossless Image Compression N Ye, M Pérez-Ortiz, RK Mantiuk 2019 Picture Coding Symposium (PCS), 1-5, 2019 | 12 | 2019 |
| Batch Group Normalization XY Zhou, J Sun, N Ye, X Lan, Q Luo, BL Lai, P Esperanca, GZ Yang, Z Li arXiv preprint arXiv:2012.02782, 2020 | 11 | 2020 |
| DeepIC: Coding for Interference Channels via Deep Learning K Chahine, N Ye, H Kim 2021 IEEE Global Communications Conference (GLOBECOM), 01-06, 2021 | 8 | 2021 |
| Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach N Ye, L Cao, L Yang, Z Zhang, Z Fang, Q Gu, GZ Yang Nature Communications Engineering 2 (1), 25, 2023 | 7 | 2023 |
| BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture N Ye, J Mei, Z Fang, Y Zhang, Z Zhang, H Wu, X Liang 2021 58th ACM/IEEE Design Automation Conference (DAC), 487-492, 2021 | 6 | 2021 |
| Semi-supervised Vein Segmentation of Ultrasound Images for Autonomous Venipuncture Y Chen, Y Wang, B Lai, Z Chen, X Cao, N Ye, Z Ren, J Zhao, XY Zhou, ... 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 6 | 2021 |
| The effect of display brightness and viewing distance: a dataset for visually lossless image compression A Mikhailiuk, N Ye, RK Mantiuk Electronic Imaging 2021 (11), 152-1-152-8, 2021 | 6 | 2021 |
| Adversarial invariant learning. In 2021 IEEE N Ye, J Tang, H Deng, XY Zhou, Q Li, Z Li, GZ Yang, Z Zhu CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12441-12449, 0 | 5 | |
