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Qizhou Wang
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2020 – today
- 2024
- [j7]Ning Li
, Ding Liu, Liguan Wang, Haiwang Ye, Qizhou Wang, Dairong Yan, Shugang Zhao:
Combination prediction of underground mine rock drilling time based on seasonal and trend decomposition using Loess. Eng. Appl. Artif. Intell. 133: 108064 (2024) - [j6]Ning Li
, Yahui Wu, Haiwang Ye, Liguan Wang, Qizhou Wang, Mingtao Jia:
Scheduling optimization of underground mine trackless transportation based on improved estimation of distribution algorithm. Expert Syst. Appl. 245: 123025 (2024) - [i19]Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang:
Do CLIPs Always Generalize Better than ImageNet Models? CoRR abs/2403.11497 (2024) - [i18]Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama:
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR abs/2406.09179 (2024) - 2023
- [j5]Ning Li
, Yahui Wu, Qizhou Wang, Haiwang Ye, Liguan Wang, Mingtao Jia, Shugang Zhao:
Underground mine truck travel time prediction based on stacking integrated learning. Eng. Appl. Artif. Intell. 120: 105873 (2023) - [c13]Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray L. Buntine
, Christopher Leckie:
Cross-Domain Graph Anomaly Detection via Anomaly-Aware Contrastive Alignment. AAAI 2023: 4676-4684 - [c12]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. ICLR 2023 - [c11]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-distribution Detection. NeurIPS 2023 - [c10]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. NeurIPS 2023 - [i17]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. CoRR abs/2303.05033 (2023) - [i16]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-Distribution Detection. CoRR abs/2311.01796 (2023) - [i15]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. CoRR abs/2311.03236 (2023) - [i14]Qizhou Wang, Guansong Pang
, Mahsa Salehi, Wray L. Buntine, Christopher Leckie:
Open-Set Graph Anomaly Detection via Normal Structure Regularisation. CoRR abs/2311.06835 (2023) - [i13]Maksim Makarenko, Qizhou Wang, Arturo Burguete-Lopez, Silvio Giancola, Bernard Ghanem, Luca Passone, Andrea Fratalocchi:
Artificial intelligence optical hardware empowers high-resolution hyperspectral video understanding at 1.2 Tb/s. CoRR abs/2312.10639 (2023) - [i12]Songming Zhang, Yuxiao Luo, Qizhou Wang, Haoang Chi, Weikai Li, Bo Han, Jinyan Li:
Are All Unseen Data Out-of-Distribution? CoRR abs/2312.16243 (2023) - 2022
- [j4]Meiyan Shu, Qizhou Wang, Shuaipeng Fei, Xiaohong Yang, Zhu Jinyu, Meng Lei, Baoguo Li, Yuntao Ma:
Improved estimation of canopy water status in maize using UAV-based digital and hyperspectral images. Comput. Electron. Agric. 197: 106982 (2022) - [j3]Chen Gong
, Qizhou Wang, Tongliang Liu
, Bo Han
, Jane You
, Jian Yang
, Dacheng Tao
:
Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 4163-4177 (2022) - [c9]Maksim Makarenko, Arturo Burguete-Lopez, Qizhou Wang, Fedor Getman, Silvio Giancola, Bernard Ghanem, Andrea Fratalocchi:
Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders. CVPR 2022: 12682-12692 - [c8]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attack Against Deep Neural Networks. NeurIPS 2022 - [c7]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. NeurIPS 2022 - [c6]Qizhou Wang, Mahsa Salehi
, Jia Shun Low, Wray L. Buntine
, Christopher Leckie:
ENDASh: Embedding Neighbourhood Dissimilarity with Attribute Shuffling for Graph Anomaly Detection. PAKDD (2) 2022: 17-29 - [i11]Maksim Makarenko, Arturo Burguete-Lopez, Qizhou Wang, Fedor Getman, Silvio Giancola, Bernard Ghanem, Andrea Fratalocchi:
Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders. CoRR abs/2204.02084 (2022) - [i10]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu
, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attacks against Deep Neural Networks. CoRR abs/2209.14826 (2022) - [i9]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu
, Bo Han:
Watermarking for Out-of-distribution Detection. CoRR abs/2210.15198 (2022) - [i8]Qizhou Wang, Guansong Pang
, Mahsa Salehi
, Wray L. Buntine, Christopher Leckie:
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment. CoRR abs/2212.01096 (2022) - 2021
- [j2]Maksim Makarenko
, Qizhou Wang
, Arturo Burguete-Lopez
, Fedor Getman
, Andrea Fratalocchi
:
Robust and Scalable Flat-Optics on Flexible Substrates via Evolutionary Neural Networks. Adv. Intell. Syst. 3(11): 2100105 (2021) - [j1]Maksim Makarenko
, Qizhou Wang, Arturo Burguete-Lopez
, Fedor Getman
, Andrea Fratalocchi
:
Robust and Scalable Flat-Optics on Flexible Substrates via Evolutionary Neural Networks. Adv. Intell. Syst. 3(11): 2170075 (2021) - [c5]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. AAAI 2021: 10183-10191 - [c4]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. AAAI 2021: 10192-10200 - [c3]Chuang Zhang, Qizhou Wang, Tengfei Liu, Xun Lu, Jin Hong, Bo Han, Chen Gong:
Fraud Detection under Multi-Sourced Extremely Noisy Annotations. CIKM 2021: 2497-2506 - [c2]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c1]Qizhou Wang, Sarah M. Erfani, Christopher Leckie, Michael E. Houle:
A Dimensionality-Driven Approach for Unsupervised Out-of-distribution Detection. SDM 2021: 118-126 - [i7]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. CoRR abs/2101.05467 (2021) - [i6]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. CoRR abs/2103.09468 (2021) - [i5]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR abs/2106.07904 (2021) - [i4]Qizhou Wang, Maksim Makarenko:
SDA-GAN: Unsupervised Image Translation Using Spectral Domain Attention-Guided Generative Adversarial Network. CoRR abs/2110.02873 (2021)
2010 – 2019
- 2019
- [i3]Jinwei Zhao, Qizhou Wang, Fuqiang Zhang, Wanli Qiu, Yufei Wang, Yu Liu, Guo Xie, Weigang Ma, Bin Wang, Xinhong Hei:
A game method for improving the interpretability of convolution neural network. CoRR abs/1910.09090 (2019) - 2018
- [i2]Jinwei Zhao, Qizhou Wang, Yufei Wang, Xinhong Hei, Yu Liu:
How far from automatically interpreting deep learning. CoRR abs/1811.07747 (2018) - [i1]Jinwei Zhao, Qizhou Wang, Yufei Wang, Xinhong Hei, Yu Liu, Zhenghao Shi:
How to improve the interpretability of kernel learning. CoRR abs/1811.10469 (2018)
Coauthor Index
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