Publications

Artificial General Intelligence

Conferences

1. Jiafei Duan, Samson Yu, Soujanya Poria, Bihan Wen, Cheston Tan: Pip: Physical Interaction Prediction via Mental Imagery with Span Selection. ECCV 2022, In Press.

2. Shantanu Jaiswal, Basura Fernando, Cheston TanTDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs. ECCV 2022, In Press.

3. Yunlu Chen, Basura Fernando, Hakan Bilen, Matthias Nießner, Efstratios Gavves: 3D Equivariant Graph Implicit Functions. ECCV 2022, In Press.

4. Kian Boon Koh, Basura Fernando: Consistency Regularization for Domain Adaptation. BMVC 2022, In Press.

5. Hehe Fan, Xiaojun Chang, Wanyue Zhang, Yi Cheng, Ying Sun, Mohan Kankanhalli: Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels. CVPR 2022: 6377-6386.

6. Bowen Xing, Ivor W. Tsang: DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition. ACL 2022: 3611-3621.

7. Bowen Xing, Ivor W. Tsang: Neural Subgraph Explorer: Reducing Noisy Information via Target-oriented Syntax Graph Pruning. IJCAI 2022: 4425-4431.

8. Jiafei Duan, Arijit Dasgupta, Jason Fischer, Cheston Tan: A Survey on Machine Learning Approaches for Modelling Intuitive Physics. IJCAI 2022: 5444-5452.

9. Philipp Bomatter, Mengmi Zhang, Dimitar Karev, Spandan Madan, Claire Tseng, Gabriel Kreiman: When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. ICCV 2021: 255-264.

10. Jiafei Duan, Samson Yu, Cheston Tan: Space: A Simulator for Physical Interactions and Causal Learning in 3D Environments. ICCV2021: 2058-2063.

11. Qianli Xu, Nicolas Gauthier, Wenyu Liang, Fen Fang, Hui Li Tan, Ying Sun, Yan Wu, Liyuan Li, Joo-Hwee Lim: TAILOR: Teaching with Active and Incremental Learning for Object Registration. AAAI2021: 16120-16123.

12. Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu, Jeremy M. Wolfe, Gabriel Kreiman:
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. NeurIPS 2021: 6946-6959.

13. Qianli Xu, Fen Fang, Ana Molino, Vigneshwaran Subbaraju, Joo-Hwee Lim: Predicting Event Memorability from Contextual Visual Semantics. NeurIPS 2021: 22431-22442.

14. Tiantian He, Yew Soon Ong, and Lu Bai: Learning Conjoint Attentions for Graph Neural Nets. NeurIPS 2021: 2641-2653.

15. Yueming Lyu, Ivor W. Tsang: Black-Box Optimizer with Stochastic Implicit Natural Gradient. ECML/PKDD 2021: 217-232.

16. Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang: ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting. KDD 2021: 269-278.

17. Yang Zhang, Ivor W. Tsang, Yawei Luo, Chang-Hui Hu, Xiaobo Lu, Xin Yu: Copy and Paste GAN: Face Hallucination From Shaded Thumbnails. CVPR 2020: 7353-7362.

18. Mengmi Zhang, Claire Tseng, Gabriel Kreiman: Putting Visual Object Recognition in Context. CVPR 2020: 12982-12991.

19. Maosen Li, Siheng Chen, Ya Zhang, Ivor W. Tsang: Graph Cross Networks with Vertex Infomax Pooling. NeurIPS 2020.

20. Yueming Lyu, Yuan Yuan, Ivor W. Tsang: Subgroup-based Rank-1 Lattice Quasi-Monte Carlo. NeurIPS 2020.

21. Xingrui Yu, Yueming Lyu, Ivor W. Tsang: Intrinsic Reward Driven Imitation Learning via Generative Model. ICML 2020: 10925-10935.

Journals

1. Mojtaba Shakeri, Erfan Miahi, Abhishek Gupta, and Yew-Soon Ong: "Scalable Transfer Evolutionary Optimization: Coping with Big Task-Instances." IEEE Transactions on Cybernetics, In Press, 2022.

2. Yinghua Yao, Yuangang Pan, Ivor W. Tsang, and Xin Yao: "Differential-Critic GAN: Generating What You Want by a Cue of Preferences.” IEEE Transactions on Neural Networks and Learning Systems, In Press, 2022.

3. Bowen Xing, and Ivor W. Tsang: "Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis." Journal of Artificial Intelligent Research 74 (2022): 627-659.

4. Abhishek Gupta, Lei Zhou, Yew-Soon Ong, Zefeng Chen, and Yaqing Hou. "Half a Dozen Real-World Applications of Evolutionary Multitasking, and More." IEEE Computational Intelligence Magazine 17, no.2 (2022): 49-66.

5. Wong, Jian Cheng, Chinchun Ooi, Abhishek Gupta, and Yew-Soon Ong. "Learning in Sinusoidal Spaces with Physics-Informed Neural Networks." IEEE Transactions on Artificial Intelligence (2022).

6. Yi Cheng, Ying Sun, Hehe Fan, Tao Zhuo, Joo-Hwee Lim, and Mohan Kankanhalli. "Entropy Guided Attention Network for Weakly-supervised Action Localization." Pattern Recognition 129 (2022): 108718.

7. Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, and Cheston Tan. "A Survey of Embodied AI: From Simulators to Research Tasks." IEEE Transactions on Emerging Topics in Computational Intelligence (2022).

8. Mengmi Zhang, and Kreiman Gabriel. "Beauty is in the Eye of the Machine." Nature Human Behaviour 5.6 (2021): 675-676.

9. Mitra Tajrobehkar, Kaihua Tang, Hanwang Zhang, and Joo-Hwee Lim. "Align R-CNN: A Pairwise Head Network for Visual Relationship Detection." IEEE Transactions on Multimedia 24 (2021): 1266-1276.

10. Jian Cheng Wong, Abhishek Gupta, and Yew-Soon Ong. "Can Transfer Neuro Evolution Tractably Solve your Differential Equations?" IEEE Computational Intelligence Magazine 16, no. 2 (2021): 14-30.

11. Lu Bai, Wu Lin, Abhishek Gupta, and Yew-Soon Ong. "From Multitask Gradient Descent to Gradient-free Evolutionary Multitasking: A Proof of Faster Convergence." IEEE Transactions on Cybernetics (2021).

12. Xiaofeng Cao, and Ivor W. Tsang. "Distribution Disagreement via Lorentzian Focal Representation." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).

13. Yang Zhang, Ivor W. Tsang, Yawei Luo, Changhui Hu, Xiaobo Lu, and Xin Yu. "Recursive Copy and Paste GAN: Face Hallucination from Shaded Thumbnails." IEEE Transactions on Pattern Analysis and Machine Intelligence 44, no. 8 (2021): 4321-4338.

14. Weiwei Liu, Haobo Wang, Xiaobo Shen, and Ivor W. Tsang. "The Emerging Trends of Multi-label Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).

15. Xiaofeng Cao, and Ivor W. Tsang. "Distribution Disagreement via Lorentzian Focal Representation." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).

16. Xiaofeng Xu, Ivor W. Tsang, and Chuancai Liu. "Improving Generalization via Attribute Selection on Out-of-the-Box Data." Neural Computation 32, no. 2 (2020): 485-514.

17. Yaxin Shi, Yuangang Pan, Donna Xu, and Ivor W. Tsang. "Multiview Alignment and Generation in CCA via Consistent Latent Encoding." Neural Computation 32, no. 10 (2020): 1936-1979.

18. Mengmi Zhang, Jiashi Feng, Keng Teck Ma, Joo Hwee Lim, Qi Zhao, and Gabriel Kreiman. "Finding Any Waldo with Zero-shot Invariant and Efficient Visual Search." Nature Communications 9.1 (2018): 1-15.

Resilient & Safe AI

Conferences

1. Alvin Chan, Yew Soon Ong, Clement Tan: How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? IJCAI 2022: 659-665.

2. Zhiwei Hu, Victor Gutiérrez-Basulto, Zhiliang Xiang, Xiaoli Li, Ru Li, Jeff Z. Pan: Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs. IJCAI 2022: 3078-3084.

3. Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, Bryan Kian Hsiang Low: Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. ICML 2022: 21180-21204.

4. Xinghua Qu, Pengfei Wei, Mingyong Gao, Zhu Sun, Yew Soon Ong, Zejun Ma: Synthesising Audio Adversarial Examples for Automatic Speech Recognition. KDD 2022: 1430-1440.

5. Xinghua Qu, Yew Soon Ong, Abhishek Gupta, Pengfei Wei, Zhu Sun, Zejun Ma: Importance Prioritized Policy Distillation. KDD 2022: 1420-1429.

6. Kangkang Lu, Cuong Manh Nguyen, Xun Xu, Kiran Chari, Yu Jing Goh, Chuan-Sheng FooARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity. ICLR 2021.

7. Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low: Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. NeurIPS 2021: 1007-1021.

8. Jiawei Du, Hu Zhang, Joey Tianyi Zhou, Yi Yang, Jiashi Feng: Query-efficient Meta Attack to Deep Neural Networks. ICLR 2020.

9. Di Jin, Zhijing Jin, Joey Tianyi Zhou, Peter Szolovits: Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment. AAAI 2020: 8018-8025.

10. Yueming Lyu, Ivor W. Tsang: Curriculum Loss: Robust Learning and Generalization against Label Corruption. ICLR 2020.

11. Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML 2020: 4006-4016. 

12. Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, Vijay Chandrasekhar: Adversarially Learned Anomaly Detection. ICDM 2018: 727-736.

Journals

1. Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, and Masashi Sugiyama. "Fast and Robust Rank Aggregation against Model Misspecification." Journal of Machine Learning Research 23 (2022): 23-1.

2. Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, and Joey Tianyi Zhou. "XAI Beyond Classification: Interpretable Neural Clustering." Journal of Machine Learning Research 23 (2022): 6-1.

3. Zongbo Han, Changqing Zhang, Huazhu Fu, and Joey Tianyi Zhou. "Trusted Multi-View Classification with Dynamic Evidential Fusion." IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).

4. Astha Garg, Wenyu Zhang, Jules Samaran, Ramasamy Savitha, and Chuan-Sheng Foo. "An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series." IEEE Transactions on Neural Networks and Learning Systems 33, no. 6 (2021): 2508-2517.

5. Yuangang Pan, Ivor W. Tsang, Yueming Lyu, Avinash K. Singh, and Chin-Teng Lin. "Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation." Neural Computation 33, no. 6 (2021): 1616-1655.

6. Xinghua Qu, Yew-Soon Ong, and Abhishek Gupta. "Frame-correlation Transfers Trigger Economical Attacks on Deep Reinforcement Learning Policies." IEEE Transactions on Cybernetics (2021).

7. Xinghua Qiu, Abhishek Gupta, Yew-Soon Ong, and Zhu Sun. "Adversary Agnostic Robust Deep Reinforcement Learning." IEEE Transactions on Neural Networks and Learning Systems (2021).

8. Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya Zhang, and Ivor W. Tsang. "Learning on Attribute-missing Graphs." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).

9. Huiting Hong, Xin Li, Yuangang Pan, and Ivor Tsang. "Domain-adversarial Network Alignment." IEEE Transactions on Knowledge and Data Engineering (2020).

10. Xiaofeng Cao, and Ivor W. Tsang. "Shattering Distribution for Active Learning." IEEE Transactions on Neural Networks and Learning Systems (2020).

11. Bo Han, Ivor W. Tsang, Xiaokui Xiao, Ling Chen, Sai-Fu Fung, and P. Yu Celina. "Privacy-preserving Stochastic Gradual Learning." IEEE Transactions on Knowledge and Data Engineering 33, no. 8 (2020): 3129-3140.

12. Yuangang Pan, Ivor W. Tsang, Avinash K. Singh, Chin-Teng Lin, and Masashi Sugiyama. "Stochastic Multichannel Ranking with Brain Dynamics Preferences." Neural Computation 32, no. 8 (2020): 1499-1530.

13. Yanxin Zhang, Yulei Sui, Shirui Pan, Zheng Zheng, Baodi Ning, Ivor W. Tsang, and Wanlei Zhou. "Familial Clustering for Weakly-labeled Android Malware Using Hybrid Representation Learning." IEEE Transactions on Information Forensics and Security 15 (2019): 3401-3414.

14. Jing Li, Yuangang Pan, Yulei Sui, and Ivor W. Tsang. "Secure Metric Learning via Differential Pairwise Privacy." IEEE Transactions on Information Forensics and Security 15 (2020): 3640-3652.

15. Defu Liu; Ivor W. Tsang, Guowu Yang. “A Convergence Path to Deep Learning on Noisy Labels." IEEE Transactions on Neural Networks and Learning Systems. In Press.

Sustainable AI

Conferences

1. Wenyu Zhang, Li Shen, Wanyue Zhang, Chuan-Sheng Foo: Few-shot Adaptation of Pre-trained Networks for Domain Shift. IJCAI 2022: 1665-1671.

Journals 

1. Yang He, Ping Liu, Linchao Zhu, and Yi Yang. "Filter Pruning by Switching to Neighboring CNNs with Good Attributes." IEEE Transactions on Neural Networks and Learning Systems (2022).

2. Zhehui Wang, Tao Luo, Rick Siow Mong Goh, and Joey Tianyi Zhou. "EDCompress: Energy-aware Model Compression for Dataflows." IEEE Transactions on Neural Networks and Learning Systems (2022).

3. Tian Huang, Tao Luo, Ming Yan and Joey Tianyi Zhou, and Rick Siow Mong Goh. “RCT: Resource Constrained Training for Edge AI." IEEE Transactions on Neural Networks and Learning Systems (2022).

4. Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, and Yew-Soon Ong. "A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges." arXiv preprint arXiv:2205.03824 (2022).

5. Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li. "Self-supervised Autoregressive Domain Adaptation for Time Series Data." IEEE Transactions on Neural Networks and Learning Systems (2022).

6. Qing Xu, Zhenghua Chen, Keyu Wu, Chao Wang, Min Wu, and Xiaoli Li. "KDnet-RUL: A Knowledge Distillation Framework to Compress Deep Neural Networks for Machine Remaining Useful Life Prediction." IEEE Transactions on Industrial Electronics 69, no. 2 (2021): 2022-2032.

7. Pan Lai, Rui Fan, Xiao Zhang, Wei Zhang, Fang Liu, and Joey Tianyi Zhou. "Utility Optimal Thread Assignment and Resource Allocation in Multi-server Systems." IEEE/ACM Transactions on Networking 30, no. 2 (2021): 735-748.

8. Joey Tianyi Zhou, Heng Zhao, Xi Peng, Meng Fang, Zheng Qin, and Rick Siow Mong Goh. "Transfer Hashing: From Shallow to Deep." IEEE Transactions on Neural Networks and Learning Systems 29, no. 12 (2018): 6191-6201.

Others

Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Wenyu Zhang, Li Shen, Wanyue Zhang and Chuan-Sheng Foo

Type-aware Embedding for Multi-Hop Reasoning over Knowledge Graphs
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Xiaoli Li, Ru Li, Jeff Z. Pan

A Survey on Machine Learning Approaches for Modelling Intuitive Physics
Jiafei Duan, Arijit Dasgupta, Jason Fischer, Cheston Tan

A Survey of Embodied Al: From Simulators to Research Tasks
Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu and Cheston Tan