Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDNinghao Liu
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j13]Ruixiang Tang, Qizhang Feng, Ninghao Liu, Fan Yang, Xia Hu:
Did You Train on My Dataset? Towards Public Dataset Protection with CleanLabel Backdoor Watermarking. SIGKDD Explor. 25(1): 43-53 (2023)- [j12]Mingyang Wan
, Daochen Zha, Ninghao Liu, Na Zou:
In-Processing Modeling Techniques for Machine Learning Fairness: A Survey. ACM Trans. Knowl. Discov. Data 17(3): 35:1-35:27 (2023) - [j11]Shuang Zhou, Xiao Huang, Ninghao Liu, Huachi Zhou, Fu-Lai Chung, Long-Kai Huang:
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation. IEEE Trans. Knowl. Data Eng. 35(12): 12721-12735 (2023) - [c44]Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li:
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution. AAAI 2023: 7441-7449 - [c43]Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang:
SEAT: Stable and Explainable Attention. AAAI 2023: 12907-12915 - [c42]Xuansheng Wu, Xinyu He, Tianming Liu, Ninghao Liu, Xiaoming Zhai:
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-Shot Prompt Learning for Automatic Scoring in Science Education. AIED 2023: 401-413 - [c41]Zihan Guan, Lichao Sun, Mengnan Du, Ninghao Liu:
Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks. CIKM 2023: 608-618 - [c40]Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li, Ninghao Liu:
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction. CIKM 2023: 2259-2269 - [c39]Zihan Guan, Mengnan Du, Ninghao Liu:
XGBD: Explanation-Guided Graph Backdoor Detection. ECAI 2023: 932-939 - [c38]Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Ben Hu:
DIVISION: Memory Efficient Training via Dual Activation Precision. ICML 2023: 36036-36057 - [c37]Yucheng Shi, Kaixiong Zhou, Ninghao Liu:
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning. ECML/PKDD (3) 2023: 104-121 - [c36]Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Ben Hu:
Mitigating Algorithmic Bias with Limited Annotations. ECML/PKDD (2) 2023: 241-258 - [c35]Kaixiong Zhou, Soo-Hyun Choi, Zirui Liu, Ninghao Liu, Fan Yang, Rui Chen, Li Li, Xia Hu:
Adaptive Label Smoothing To Regularize Large-Scale Graph Training. SDM 2023: 55-63 - [c34]Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu:
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection. WSDM 2023: 625-633 - [c33]Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, Xia Hu:
S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking. WSDM 2023: 787-795 - [c32]Qing Li, Xiao Huang, Ninghao Liu, Yuxiao Dong, Guansong Pang:
International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning. WSDM 2023: 1273-1274 - [i61]Xuansheng Wu, Xinyu He, Tianming Li, Ninghao Liu, Xiaoming Zhai:
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education. CoRR abs/2301.08771 (2023) - [i60]Xuansheng Wu, Zhiyi Zhao, Ninghao Liu:
NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation. CoRR abs/2302.12903 (2023) - [i59]Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Zihao Wu, Lin Zhao, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, Hongmin Cai, Quanzheng Li, Dinggang Shen, Tianming Liu, Xiang Li:
ChatAug: Leveraging ChatGPT for Text Data Augmentation. CoRR abs/2302.13007 (2023) - [i58]Xuansheng Wu, Kaixiong Zhou, Mingchen Sun, Xin Wang, Ninghao Liu:
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges. CoRR abs/2303.07275 (2023) - [i57]Ruixiang Tang, Qizhang Feng, Ninghao Liu, Fan Yang, Xia Hu:
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking. CoRR abs/2303.11470 (2023) - [i56]Yucheng Shi, Mengnan Du, Xuansheng Wu, Zihan Guan, Ninghao Liu:
Black-box Backdoor Defense via Zero-shot Image Purification. CoRR abs/2303.12175 (2023) - [i55]Guoyu Lu, Sheng Li, Gengchen Mai, Jin Sun, Dajiang Zhu, Lilong Chai, Haijian Sun, Xianqiao Wang, Haixing Dai, Ninghao Liu, Rui Xu, Daniel Petti, Changying Li, Tianming Liu:
AGI for Agriculture. CoRR abs/2304.06136 (2023) - [i54]Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song, Deepak Mishra, Ninghao Liu, Song Gao, Tianming Liu, Gao Cong, Yingjie Hu, Chris Cundy, Ziyuan Li, Rui Zhu, Ni Lao:
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence. CoRR abs/2304.06798 (2023) - [i53]Guanchu Wang, Ninghao Liu, Daochen Zha, Xia Ben Hu:
Interactive System-wise Anomaly Detection. CoRR abs/2304.10704 (2023) - [i52]Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu, Guoyu Lu, Sheng Li, Tianming Liu, Xiaoming Zhai:
Artificial General Intelligence (AGI) for Education. CoRR abs/2304.12479 (2023) - [i51]Zihan Guan, Mengxuan Hu, Zhongliang Zhou, Jielu Zhang, Sheng Li, Ninghao Liu:
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks. CoRR abs/2305.03289 (2023) - [i50]Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu:
DEGREE: Decomposition Based Explanation For Graph Neural Networks. CoRR abs/2305.12895 (2023) - [i49]Ziqi Zhao, Yucheng Shi, Shushan Wu, Fan Yang, Wenzhan Song, Ninghao Liu:
Interpretation of Time-Series Deep Models: A Survey. CoRR abs/2305.14582 (2023) - [i48]Yao Rong, Guanchu Wang, Qizhang Feng, Ninghao Liu, Zirui Liu, Enkelejda Kasneci, Xia Ben Hu:
Efficient GNN Explanation via Learning Removal-based Attribution. CoRR abs/2306.05760 (2023) - [i47]Shuang Zhou, Xiao Huang, Ninghao Liu, Huachi Zhou, Fu-Lai Chung, Long-Kai Huang:
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation. CoRR abs/2306.10534 (2023) - [i46]Saed Rezayi, Zhengliang Liu, Zihao Wu, Chandra Dhakal, Bao Ge, Haixing Dai, Gengchen Mai, Ninghao Liu, Chen Zhen, Tianming Liu, Sheng Li:
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications. CoRR abs/2306.11892 (2023) - [i45]Xuansheng Wu, Huachi Zhou, Wenlin Yao, Xiao Huang, Ninghao Liu:
Towards Personalized Cold-Start Recommendation with Prompts. CoRR abs/2306.17256 (2023) - [i44]Yucheng Shi, Kaixiong Zhou, Ninghao Liu:
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning. CoRR abs/2307.01053 (2023) - [i43]Haixing Dai, Lu Zhang, Lin Zhao, Zihao Wu, Zhengliang Liu, David Liu, Xiaowei Yu, Yanjun Lyu, Changying Li, Ninghao Liu, Tianming Liu, Dajiang Zhu:
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification. CoRR abs/2307.04343 (2023) - [i42]Zihan Guan, Zihao Wu, Zhengliang Liu, Dufan Wu, Hui Ren, Quanzheng Li, Xiang Li, Ninghao Liu:
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study. CoRR abs/2307.11346 (2023) - [i41]Zihan Guan, Mengnan Du, Ninghao Liu:
XGBD: Explanation-Guided Graph Backdoor Detection. CoRR abs/2308.04406 (2023) - [i40]Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li, Ninghao Liu:
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction. CoRR abs/2308.09663 (2023) - [i39]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) - [i38]Fei Dou, Jin Ye, Geng Yuan, Qin Lu, Wei Niu, Haijian Sun, Le Guan, Guoyu Lu, Gengchen Mai, Ninghao Liu, Jin Lu, Zhengliang Liu, Zihao Wu, Chenjiao Tan, Shaochen Xu, Xianqiao Wang, Guoming Li, Lilong Chai, Sheng Li, Jin Sun, Hongyue Sun, Yunli Shao, Changying Li, Tianming Liu, Wenzhan Song:
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges. CoRR abs/2309.07438 (2023) - [i37]Zirui He, Huiqi Deng, Haiyan Zhao, Ninghao Liu, Mengnan Du:
Mitigating Shortcuts in Language Models with Soft Label Encoding. CoRR abs/2309.09380 (2023) - [i36]Zhengliang Liu, Peilong Wang, Yiwei Li, Jason Holmes, Peng Shu, Lian Zhang, Chenbin Liu, Ninghao Liu, Dajiang Zhu, Xiang Li, Quanzheng Li, Samir H. Patel, Terence T. Sio, Tianming Liu, Wei Liu:
RadOnc-GPT: A Large Language Model for Radiation Oncology. CoRR abs/2309.10160 (2023) - [i35]Yucheng Shi, Shaochen Xu, Zhengliang Liu, Tianming Liu, Xiang Li, Ninghao Liu:
MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases. CoRR abs/2309.16035 (2023) - [i34]Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan, Xiaoyang Wang, Ninghao Liu, Dong Yu:
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning. CoRR abs/2310.00492 (2023) - [i33]Chenxu Zhao, Wei Qian, Yucheng Shi, Mengdi Huai, Ninghao Liu:
Automated Natural Language Explanation of Deep Visual Neurons with Large Models. CoRR abs/2310.10708 (2023) - [i32]Hua Tang, Lu Cheng, Ninghao Liu, Mengnan Du:
A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier. CoRR abs/2310.12785 (2023) - [i31]Zhengliang Liu, Yiwei Li, Qian Cao, Junwen Chen, Tianze Yang, Zihao Wu, John Hale, John Gibbs, Khaled Rasheed, Ninghao Liu, Gengchen Mai, Tianming Liu:
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities. CoRR abs/2310.19626 (2023) - [i30]Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang:
Improving Faithfulness for Vision Transformers. CoRR abs/2311.17983 (2023) - 2022
- [j10]Ruixiang Tang, Ninghao Liu, Fan Yang, Na Zou, Xia Hu:
Defense Against Explanation Manipulation. Frontiers Big Data 5: 704203 (2022) - [j9]Weijie Fu, Meng Wang, Mengnan Du, Ninghao Liu, Shijie Hao, Xia Hu:
Differentiated Explanation of Deep Neural Networks With Skewed Distributions. IEEE Trans. Pattern Anal. Mach. Intell. 44(6): 2909-2922 (2022) - [c31]Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang:
AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training. CIKM 2022: 2046-2055 - [c30]Zhou Yang, Ninghao Liu, Xia Ben Hu, Fang Jin:
Tutorial on Deep Learning Interpretation: A Data Perspective. CIKM 2022: 5156-5159 - [c29]Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu:
DEGREE: Decomposition Based Explanation for Graph Neural Networks. ICLR 2022 - [c28]Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu:
G-Mixup: Graph Data Augmentation for Graph Classification. ICML 2022: 8230-8248 - [c27]Weihao Song, Yushun Dong, Ninghao Liu, Jundong Li:
GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks. KDD 2022: 1625-1634 - [c26]Shoujin Wang, Ninghao Liu, Xiuzhen Zhang, Yan Wang, Francesco Ricci, Bamshad Mobasher:
Data Science and Artificial Intelligence for Responsible Recommendations. KDD 2022: 4904-4905 - [c25]Shuang Zhou, Xiao Huang, Ninghao Liu, Qiaoyu Tan, Fu-Lai Chung:
Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning. SDM 2022: 262-270 - [c24]Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu:
Geometric Graph Representation Learning via Maximizing Rate Reduction. WWW 2022: 1226-1237 - [c23]Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li:
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks. WWW 2022: 1259-1269 - [i29]Qiaoyu Tan, Ninghao Liu, Xiao Huang, Rui Chen, Soo-Hyun Choi, Xia Hu:
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs. CoRR abs/2201.02534 (2022) - [i28]Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu:
Geometric Graph Representation Learning via Maximizing Rate Reduction. CoRR abs/2202.06241 (2022) - [i27]Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu:
G-Mixup: Graph Data Augmentation for Graph Classification. CoRR abs/2202.07179 (2022) - [i26]Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Ben Hu:
Mitigating Algorithmic Bias with Limited Annotations. CoRR abs/2207.10018 (2022) - [i25]Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Ben Hu:
Towards Memory Efficient Training via Dual Activation Precision. CoRR abs/2208.04187 (2022) - [i24]Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang:
SEAT: Stable and Explainable Attention. CoRR abs/2211.13290 (2022) - [i23]Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li:
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution. CoRR abs/2211.14383 (2022) - [i22]Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu:
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection. CoRR abs/2212.12488 (2022) - 2021
- [j8]Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu:
Learning credible DNNs via incorporating prior knowledge and model local explanation. Knowl. Inf. Syst. 63(2): 305-332 (2021) - [j7]Fan Yang, Ninghao Liu, Mengnan Du, Xia Hu:
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation. SIGKDD Explor. 23(1): 59-68 (2021) - [j6]Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu, Xia Hu:
Adversarial Attacks and Defenses: An Interpretation Perspective. SIGKDD Explor. 23(1): 86-99 (2021) - [c22]Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu:
Dynamic Memory based Attention Network for Sequential Recommendation. AAAI 2021: 4384-4392 - [c21]Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu:
Sparse-Interest Network for Sequential Recommendation. WSDM 2021: 598-606 - [i21]Fan Yang, Ninghao Liu, Mengnan Du, Xia Hu:
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation. CoRR abs/2101.06930 (2021) - [i20]Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu:
Sparse-Interest Network for Sequential Recommendation. CoRR abs/2102.09267 (2021) - [i19]Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu:
Dynamic Memory based Attention Network for Sequential Recommendation. CoRR abs/2102.09269 (2021) - [i18]Raj Vardhan, Ninghao Liu, Phakpoom Chinprutthiwong, Weijie Fu, Zhenyu Hu, Xia Ben Hu, Guofei Gu:
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection. CoRR abs/2103.11526 (2021) - [i17]Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li:
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks. CoRR abs/2108.05233 (2021) - [i16]Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu:
Adaptive Label Smoothing To Regularize Large-Scale Graph Training. CoRR abs/2108.13555 (2021) - [i15]Mingyang Wan, Daochen Zha, Ninghao Liu, Na Zou:
Modeling Techniques for Machine Learning Fairness: A Survey. CoRR abs/2111.03015 (2021) - [i14]Ruixiang Tang, Ninghao Liu, Fan Yang, Na Zou, Xia Hu:
Defense Against Explanation Manipulation. CoRR abs/2111.04303 (2021) - 2020
- [j5]Mengnan Du, Ninghao Liu, Xia Hu:
Techniques for interpretable machine learning. Commun. ACM 63(1): 68-77 (2020) - [j4]Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, Xia Hu:
A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter. BMC Medical Informatics Decis. Mak. 20-S(4): 254 (2020) - [c20]Ninghao Liu, Yong Ge, Li Li, Xia Hu, Rui Chen, Soo-Hyun Choi:
Explainable Recommender Systems via Resolving Learning Representations. CIKM 2020: 895-904 - [c19]Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu:
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks. KDD 2020: 218-228 - [c18]Kion Fallah, Adam Willats, Ninghao Liu, Christopher Rozell:
Learning sparse codes from compressed representations with biologically plausible local wiring constraints. NeurIPS 2020 - [c17]Fan Yang, Ninghao Liu, Mengnan Du, Kaixiong Zhou, Shuiwang Ji, Xia Hu:
Deep Neural Networks with Knowledge Instillation. SDM 2020: 370-378 - [c16]Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu:
Learning to Hash with Graph Neural Networks for Recommender Systems. WWW 2020: 1988-1998 - [i13]Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu:
Learning to Hash with Graph Neural Networks for Recommender Systems. CoRR abs/2003.01917 (2020) - [i12]Ninghao Liu, Mengnan Du, Xia Hu:
Adversarial Machine Learning: An Interpretation Perspective. CoRR abs/2004.11488 (2020) - [i11]Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu:
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks. CoRR abs/2006.08131 (2020) - [i10]Ninghao Liu, Yong Ge, Li Li, Xia Hu, Rui Chen, Soo-Hyun Choi:
Explainable Recommender Systems via Resolving Learning Representations. CoRR abs/2008.09316 (2020) - [i9]Ninghao Liu, Yunsong Meng, Xia Hu, Tie Wang, Bo Long:
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability. CoRR abs/2009.07494 (2020)
2010 – 2019
- 2019
- [j3]Qiaoyu Tan, Ninghao Liu, Xia Hu:
Deep Representation Learning for Social Network Analysis. Frontiers Big Data 2: 2 (2019) - [c15]Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu:
Learning Credible Deep Neural Networks with Rationale Regularization. ICDM 2019: 150-159 - [c14]Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu:
Deep Structured Cross-Modal Anomaly Detection. IJCNN 2019: 1-8 - [c13]Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu:
Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding. KDD 2019: 932-940 - [c12]Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, Xia Hu:
Identification of Cancer Survivors Living with PTSD on Social Media. MedInfo 2019: 1468-1469 - [c11]Yin Zhang, Ninghao Liu, Shuiwang Ji, James Caverlee, Xia Hu:
An Interpretable Neural Model with Interactive Stepwise Influence. PAKDD (3) 2019: 528-540 - [c10]Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, Xia Hu:
Using Deep Neural Network to Identify Cancer Survivors Living with Post-Traumatic Stress Disorder on Social Media. SEPDA@ISWC 2019: 48-52 - [c9]Ninghao Liu, Mengnan Du, Xia Hu:
Representation Interpretation with Spatial Encoding and Multimodal Analytics. WSDM 2019: 60-68 - [c8]Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, Xia Hu:
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition. WWW 2019: 383-393 - [i8]Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, Xia Hu:
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition. CoRR abs/1903.11245 (2019) - [i7]Qiaoyu Tan, Ninghao Liu, Xia Hu:
Deep Representation Learning for Social Network Analysis. CoRR abs/1904.08547 (2019) - [i6]Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu:
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding. CoRR abs/1905.10668 (2019) - [i5]Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu:
Deep Structured Cross-Modal Anomaly Detection. CoRR abs/1908.03848 (2019) - [i4]Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu:
Learning Credible Deep Neural Networks with Rationale Regularization. CoRR abs/1908.05601 (2019) - 2018
- [c7]Fan Yang, Ninghao Liu, Suhang Wang, Xia Hu:
Towards Interpretation of Recommender Systems with Sorted Explanation Paths. ICDM 2018: 667-676 - [c6]Ninghao Liu, Donghwa Shin, Xia Hu:
Contextual Outlier Interpretation. IJCAI 2018: 2461-2467 - [c5]Mengnan Du, Ninghao Liu, Qingquan Song, Xia Hu:
Towards Explanation of DNN-based Prediction with Guided Feature Inversion. KDD 2018: 1358-1367 - [c4]Ninghao Liu, Hongxia Yang, Xia Hu:
Adversarial Detection with Model Interpretation. KDD 2018: 1803-1811 - [c3]Ninghao Liu, Xiao Huang, Jundong Li, Xia Hu:
On Interpretation of Network Embedding via Taxonomy Induction. KDD 2018: 1812-1820 - [r1]Ninghao Liu,