Han, Tian

22 publications

WACV 2024 Enforcing Sparsity on Latent Space for Robust and Explainable Representations Hanao Li, Tian Han

ICML 2024 Improving Adversarial Energy-Based Model via Diffusion Process Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Søren Hauberg, Bo Li

ICML 2024 Layerwise Change of Knowledge in Neural Networks Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han, Quanshi Zhang

ICML 2024 Learning Latent Space Hierarchical EBM Diffusion Models Jiali Cui, Tian Han

ECCV 2024 Learning Multimodal Latent Generative Models with Energy-Based Prior Shiyu Yuan, Jiali Cui, Hanao Li, Tian Han

NeurIPS 2023 Learning Energy-Based Model via Dual-MCMC Teaching Jiali Cui, Tian Han

ICCV 2023 Learning Hierarchical Features with Joint Latent Space Energy-Based Prior Jiali Cui, Ying Nian Wu, Tian Han

CVPR 2023 Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator Jiali Cui, Ying Nian Wu, Tian Han

UAI 2023 Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu

NeurIPS 2022 Adaptive Multi-Stage Density Ratio Estimation for Learning Latent Space Energy-Based Model Zhisheng Xiao, Tian Han

AAAI 2022 Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs Chang Lu, Tian Han, Yue Ning

AAAI 2022 Learning from the Tangram to Solve Mini Visual Tasks Yizhou Zhao, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu

NeurIPSW 2020 From EM-Projections to Variational Auto-Encoder Tian Han, Jun Zhang, Ying Nian Wu

NeurIPS 2020 Learning Latent Space Energy-Based Prior Model Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

ECCV 2020 Learning Multi-Layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu

CoRL 2020 Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design Jiankai Sun, Hao Sun, Tian Han, Bolei Zhou

AAAI 2020 On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu

NeurIPSW 2020 Semi-Supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu

WACV 2019 Learning Generator Networks for Dynamic Patterns Tian Han, Yang Lu, Jiawen Wu, Xianglei Xing, Ying Nian Wu

IJCAI 2018 Replicating Active Appearance Model by Generator Network Tian Han, Jiawen Wu, Ying Nian Wu

AAAI 2017 Alternating Back-Propagation for Generator Network Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu

CVPR 2012 Parsing Façade with Rank-One Approximation Chao Yang, Tian Han, Long Quan, Chiew-Lan Tai