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Huiqi Deng
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2020 – today
- 2024
- [j4]Huilin Zhou, Jie Ren, Huiqi Deng, Xu Cheng, Jinpeng Zhang, Quanshi Zhang:
Interpretability of Neural Networks Based on Game-theoretic Interactions. Mach. Intell. Res. 21(4): 718-739 (2024) - [j3]Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang:
Unifying Fourteen Post-Hoc Attribution Methods With Taylor Interactions. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 4625-4640 (2024) - [j2]Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du:
Explainability for Large Language Models: A Survey. ACM Trans. Intell. Syst. Technol. 15(2): 20:1-20:38 (2024) - [c14]Hongbin Pei, Taile Chen, Chen A, Huiqi Deng, Jing Tao, Pinghui Wang, Xiaohong Guan:
HAGO-Net: Hierarchical Geometric Massage Passing for Molecular Representation Learning. AAAI 2024: 14572-14580 - [c13]Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, Quanshi Zhang:
Explaining Generalization Power of a DNN Using Interactive Concepts. AAAI 2024: 17105-17113 - [c12]Zirui He, Huiqi Deng, Haiyan Zhao, Ninghao Liu, Mengnan Du:
Mitigating Shortcuts in Language Models with Soft Label Encoding. LREC/COLING 2024: 11341-11348 - [c11]Hongbin Pei, Yu Li, Huiqi Deng, Jingxin Hai, Pinghui Wang, Jie Ma, Jing Tao, Yuheng Xiong, Xiaohong Guan:
Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing. ICML 2024 - [c10]Hongbin Pei, Yuheng Xiong, Pinghui Wang, Jing Tao, Jialun Liu, Huiqi Deng, Jie Ma, Xiaohong Guan:
Memory Disagreement: A Pseudo-Labeling Measure from Training Dynamics for Semi-supervised Graph Learning. WWW 2024: 434-445 - 2023
- [c9]Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang:
Defining and Quantifying the Emergence of Sparse Concepts in DNNs. CVPR 2023: 20280-20289 - [c8]Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang:
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. ICML 2023: 28889-28913 - [c7]Dongrui Liu, Huiqi Deng, Xu Cheng, Qihan Ren, Kangrui Wang, Quanshi Zhang:
Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities. NeurIPS 2023 - [i10]Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, Quanshi Zhang:
Concept-Level Explanation for the Generalization of a DNN. CoRR abs/2302.13091 (2023) - [i9]Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang:
Bayesian Neural Networks Tend to Ignore Complex and Sensitive Concepts. CoRR abs/2302.13095 (2023) - [i8]Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang:
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions. CoRR abs/2303.01506 (2023) - [i7]Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du:
Explainability for Large Language Models: A Survey. CoRR abs/2309.01029 (2023) - [i6]Zirui He, Huiqi Deng, Haiyan Zhao, Ninghao Liu, Mengnan Du:
Mitigating Shortcuts in Language Models with Soft Label Encoding. CoRR abs/2309.09380 (2023) - [i5]Xinhao Zheng, Huiqi Deng, Quanshi Zhang:
Towards Attributions of Input Variables in a Coalition. CoRR abs/2309.13411 (2023) - 2022
- [c6]Huiqi Deng, Qihan Ren, Hao Zhang, Quanshi Zhang:
Discovering and Explaining the Representation Bottleneck of DNNS. ICLR 2022 - 2021
- [c5]Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu:
A Unified Taylor Framework for Revisiting Attribution Methods. AAAI 2021: 11462-11469 - [c4]Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu:
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution. KDD 2021: 258-268 - [i4]Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu:
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution. CoRR abs/2104.06629 (2021) - [i3]Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang:
Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models. CoRR abs/2111.06206 (2021) - [i2]Huiqi Deng, Qihan Ren, Xu Chen, Hao Zhang, Jie Ren, Quanshi Zhang:
Discovering and Explaining the Representation Bottleneck of DNNs. CoRR abs/2111.06236 (2021) - 2020
- [j1]Huiqi Deng, Weifu Chen, Qi Shen, Andy Jinhua Ma, Pong C. Yuen, Guocan Feng:
Invariant subspace learning for time series data based on dynamic time warping distance. Pattern Recognit. 102: 107210 (2020) - [i1]Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu:
A Unified Taylor Framework for Revisiting Attribution Methods. CoRR abs/2008.09695 (2020)
2010 – 2019
- 2019
- [c3]Qingxiong Tan, Andy Jinhua Ma, Mang Ye, Baoyao Yang, Huiqi Deng, Vincent Wai-Sun Wong, Yee-Kit Tse, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, Jessica Yuet-Ling Ching, Francis Ka-Leung Chan, Pong C. Yuen:
UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction. CIKM 2019: 109-118 - 2018
- [c2]Qingxiong Tan, Andy Jinhua Ma, Huiqi Deng, Vincent W. S. Wong, Yee-Kit Tse, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, Ching Ling Jessica Yuet, Francis Ka-Leung Chan, Pong C. Yuen:
A Hybrid Residual Network and Long Short-Term Memory Method for Peptic Ulcer Bleeding Mortality Prediction. AMIA 2018 - [c1]Huiqi Deng, Weifu Chen, Andy Jinhua Ma, Qi Shen, Pong C. Yuen, Guocan Feng:
Robust Shapelets Learning: Transform-Invariant Prototypes. PRCV (3) 2018: 491-502
Coauthor Index
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last updated on 2024-10-01 21:43 CEST by the dblp team
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