Selected Publications [bibtex]

  1. Zequn Zeng, Jianqiao Sun, Hao Zhang#, Tiansheng Wen, Yudi Su, Yan Xie, Zhengjue Wang, and Bo Chen#, "HICEScore: A Hierarchical Metric for Image Captioning Evaluation", (#Corresponding authors) The ACM Multimedia 2024 (ACM MM) , Melbourne, Australia, October 2024. arxiv:2403.03715 / Pytorch code in GitHub
  2. Zequn Zeng, Yan Xie, Hao Zhang#, Chiyu Chen, Bo Chen#, and Zhengjue Wang, "MeaCap: Memory-Augmented Zero-shot Image Captioning", (*Equal contribution by the first two authors, #Corresponding authors) The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (CVPR2024), Seattle, US, June 2024. arxiv:2403.03715 / Pytorch code in GitHub
  3. Chengxi Zang, Hao Zhang, Jie Xu, Hansi Zhang, Sajjad Fouladvand, Shreyas Havaldar, Feixiong Cheng, Kun Chen, Yong Chen, Benjamin S Glicksberg, Jin Chen, Jiang Bian, and Fei Wang, "High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data", Nature Communications, 2023, Nature Communications / Pytorch code in GitHub
  4. M. Zhou*, H. Zhang*, Z. Bai, D. Mann-Krzisnik, F. Wang, and Y. Li, "Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures", Cell Report Methods, 2023, (*Equal contribution by the first two authors). BioArxiv / Pytorch code in GitHub
  5. H. Zheng, X. Chen, J. Yao, H. Yang, C. Li, Y. Zhang, H. Zhang, I. Tsang, J. Zhou, and M. Zhou, "Contrastive Attraction and Contrastive Repulsion for Representation Learning", Transactions on Machine Learning Research, 2023, Arxiv / OpenReview / Pytorch code in GitHub
  6. J. Maasch, H. Zhang, Q. Yang, F. Wang, and V. Kuleshov, "Regularized Data Programming with Automated Bayesian Prior Selection", The Workshop on Structured Probabilistic Inference & Generative Modeling in International Conference of Machine Learning 2023 (ICML2023), Hawaii Convention Center, US, July 2023. OpenReview
  7. Z. Zeng*, H. Zhang*, Z. Wang, R. Lu, D. Wang, and B. Chen, "ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing", The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR2023), Vancouver, Canada, June 2023. (*Equal contribution by the first two authors). arxiv:2303.02437 / Pytorch code in GitHub
  8. Z. Cheng, B. Chen, R. Lu, Z. Wang, H. Zhang, Z. Meng, and X. Yuan, "Recurrent Neural Networks for Snapshot Compressive Imaging", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 45, no. 2, pp. 2264-2281, Feb. 2023. IEEE
  9. C. Wang, B. Chen, Z. Duan, W. Chen, H. Zhang, and M. Zhou, "Generative Text Convolutional Neural Network for Hierarchical Document Representation Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 IEEE
  10. H. Zhang, C. Zang, Z. Xu, Y. Zhang, J. Xu, J. BIan, D. Morozyuk, D. Khullar, Y. Zhang, A. Nordvig, E. Schenck, E. Shenkman, R. Rothman, J. Block, K. Lyman, M. Weiner, T. Carton, F. Wang, and R. Kaushal, "Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes", Nature Medicine, 29, 26-235 (2023) Nature Medicine
  11. Y. He, C. Wang, H. Zhang, B. Chen, and M. Zhou, "A Variational Edge Partition Model for Supervised Graph Representation Learning", Thirty-sixth Conferene on Neural Information Prossing Systems (NeurIPS), New Orleans, December 2022. Arxiv
  12. H. Zhang*, L. Tian*, Z. Wang, Y. Xu, P. Cheng, K. Bai, and B. Chen, "Multiscale Visual-Attribute Co-Attention for Zero-Shot Image Recognition", IEEE Transactions on Neural Network and Learning Systems, 2021. (*Equal contribution by the first two authors). IEEE
  13. H. Zhang*, C. Wang*, Z. Wang, Z. Duan, and B. Chen, "Learning Hierarchical Document Graphs From Multilevel Sentence Relations", IEEE Transactions on Neural Network and Learning Systems, 2021. (*Equal contribution by the first two authors). IEEE
  14. Z. Duan*, H. Zhang*, C. Wang, Z. Wang, B. Chen, and M. Zhou, "EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering", The 59th Annual Meeting of the Association for Computational Linguistics (ACL), Online, Aug, 2021. (*Equal contribution by the first two authors). ACL
  15. C. Wang, B. Chen,S. Xiao, Z. Wang, H. Zhang, P. Wang, N. Han, and M. Zhou, "Multimodal Weibull Variational Autoencoder for Jointly Modeling Image-Text Data", to appear in IEEE Transactions on Cybernetics 2021+. IEEE / PDF
  16. Z. Wang*, H. Zhang*, Z. Cheng, and X. Yuan, "MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing", Conference on Computer Vision and Pattern Recognition (CVPR), Online, June, 2021. (*Equal contribution by the first two authors). arXiv:2103.01786 / Pytorch code in GitHub
  17. Z. Cheng, B. Chen, G. Liu, H. Zhang, R. Lu, Z. Wang, and X. Yuan, "Memory-Efficient Network for Large-scale Video Compressive Sensing", Conference on Computer Vision and Pattern Recognition (CVPR), Online, June, 2021. arXiv:2103.03089 / Pytorch code in GitHub
  18. He S. Yang, Yu Hou, Hao Zhang, Amy Chadburn, Lars F. Westblade, Richard Fedeli, Peter A.D. Steel, Sabrina E. Racine-Brzostek, Priya Velu, Jorge L. Sepulveda, Michael J. Satlin, Melissa M. Cushing, Rainu Kaushal, Zhen Zhao, Fei Wang, "Machine learning analysis highlights the down-trending of the proportion of COVID-19 patients with a distinct laboratory result profile", preprint on medRxiv
  19. W. Chen, B. Chen, Y. Liu, Q. Zhao, H. Zhang, L. Tian, "Max-Margin Deep Diverse Latent Dirichlet Allocation with Continual Learning", to appear in IEEE Transactions on Cybernetics 2021+. IEEE
  20. C. Wang*, H. Zhang*, B. Chen, D. Wang, Z. Wang, M. Zhou, "Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network," Thirty-fourth Conferene on Neural Information Prossing Systems (NeurIPS), Online, December 2020. PDF / NeurIPS (*Equal contribution by the first two authors)
  21. W. Chen*, C. Wang*, B. Chen, Y. Liu, H. Zhang, M. Zhou, "Bidirectional Convolutional Poisson Gamma Dynamical Systems," Thirty-fourth Conferene on Neural Information Prossing Systems (NeurIPS), Online, December 2020. PDF / NeurIPS (*Equal contribution by the first two authors)
  22. Z. Wang*, D. Duan*, H. Zhang^, C. Wang, L. Tian, B. Chen, M. Zhou, "Friendly Topic Assistant for Transformer Based Abstractive Summarization," The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, November 2020. PDF / EMNLP (*Equal contribution by the first two authors, ^Corressponding author)
  23. Z. Wang, B. Chen, R. Lu, H. Zhang, H. Liu, P. Varshney, "FusionNet: An Unsupervised Convolutionl Variational Network for Hyperspectral and Multispectral Image Fusion," to appear in IEEE Transactions on Image Processing 2020+. PDF / IEEE
  24. Z. Wang, B. Chen, H. Zhang, H. Liu, "Unsupervised Hyperspectral and Multispectral Image Fusion Based on Nonlinear Variational Probabilistic Generative Model," to appear in IEEE Transactions on Neural Network and Learning System. IEEE
  25. Z. Cheng, R. Lu, Z. Wang, H. Zhang, B. Chen, Z. Meng, X, Yuan, "BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging," European Conference on Computer Vision (ECCV2020), Online, August 2020. PDF / ECCV / Python code in GitHub
  26. S. Si, R. Wang, J. Wosik, H. Zhang, D. Dov, G. Wang, R. Henao, L. Carin, "Students Need More Attention: BERT-based Attention Model for Small Data with Application to AutomaticPatient Message Triage," Machine Learning for Healthcare (MLHC2020), Online, August 2020. PDF / arXiv:2006.11991 / Python code in GitHub
  27. H. Zhang, B. Chen, Y. Cong, D. Guo, H. Liu, and M. Zhou, "Autoencoding Topic Model with Scalable Hybrid Bayesian Inference," to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence 2020+. arXiv:2006.08804 / PDF / IEEE early access
  28. Z. Wang*, C. Wang*, H. Zhang, Z. Duan, M. Zhou, and B. Chen, "Learning dynamic hierarchical topic graph with graph convolutional network for document classification," International Conference on Artificial Intelligence and Statistics (AISTATS2020), Palermo, Sicily, Italy, June 2020. (*Equal contribution by the first two authors). PDF / AISTATS / Appendix
  29. H. Zhang, B. Chen, L. Tian, Z. Wang, and M. Zhou, "Variational hetero-encoder randomized generative adversarial networks for joint image-text modeling," International Conference on Learning Representations (ICLR2020), Addis Ababa, Ethiopia, Apr. 2020. PDF / ICLR / arXiv:1905.08622 / Code in GitHub
  30. D. Guo, B. Chen, H. Zhang, and M. Zhou, "Deep Poisson gamma dynamical systems," Neural Information Processing Systems, (NeurIPS2018), Montreal, Canada, Dec. 2018. PDF / arXiv:1810.11209 / Python (TensorFlow) Code in GitHub
  31. H. Zhang, B. Chen, D. Guo, and M. Zhou, "WHAI: Weibull hybrid autoencoding inference for deep topic modeling," International Conference on Learning Representations (ICLR2018), Vancouver, Canada, May 2018. PDF / arXiv:1803.01328 / Python (Theano) code in GitHub
  32. H. Zhang, B. Chen, Z. Wang, and H. Liu, "Deep max-margin discriminant projection," IEEE Transactions on Cybernetics, Vol. 49, No. 7, 2454-2466, July 2019. PDF / IEEE
  33. Z. Wang, B. Chen, H. Zhang, and H. Liu, "Variational Probabilistic Generative Framework for Single Image Super-Resolution," Signal Processing, 2019: 92-105. PDF / ELSEVIER
  34. B. Chen, H. Zhang, X. Zhang, W. Wen, H. Liu, and J. Liu, "Max-Margin Discriminant Projection via Data Augmentation," IEEE Transactions on Knowledge and Data Engineering, 27(7), 1964-1976, 2015. PDF / IEEE
  35. Z. Wang, Y. Wang, H. Liu, and H. Zhang, "Structured kernel dictionary learning with correlation constraint for object recognition," IEEE Transactions on Image Processing, 26(9):4578-4590, 2017. PDF / IEEE

    36. © Hao Zhang