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Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling

Oct 19, 2020
Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu

* work in progress 

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Learning Latent Space Energy-Based Prior Model for Molecule Generation

Oct 19, 2020
Bo Pang, Tian Han, Ying Nian Wu


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A Representational Model of Grid Cells Based on Matrix Lie Algebras

Jun 18, 2020
Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu


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Learning Latent Space Energy-Based Prior Model

Jun 15, 2020
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

* 22 pages, 4 figures 

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Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC

Jun 12, 2020
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu


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Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning

Jun 11, 2020
Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu

* ICML 2020. Project page: https://liqing-ustc.github.io/NGS 

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Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

Jun 10, 2020
Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu


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Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

Apr 20, 2020
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu

* Engineering, Feb, 2020 
* For high quality figures, please refer to http://wellyzhang.github.io/attach/dark.pdf 

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Generative PointNet: Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification

Apr 02, 2020
Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu


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Learning Deep Generative Models with Short Run Inference Dynamics

Dec 14, 2019
Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu


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Flow Contrastive Estimation of Energy-Based Models

Dec 02, 2019
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu


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Representation Learning: A Statistical Perspective

Nov 26, 2019
Jianwen Xie, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

* Annual Review of Statistics and Its Application 2020 

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Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns

Nov 26, 2019
Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

* The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 

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Deep Unsupervised Clustering with Clustered Generator Model

Nov 19, 2019
Dandan Zhu, Tian Han, Linqi Zhou, Xiaokang Yang, Ying Nian Wu


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Learning Energy-based Spatial-Temporal Generative ConvNets for Dynamic Patterns

Sep 26, 2019
Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

* IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019 

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Towards Interpretable Image Synthesis by Learning Sparsely Connected AND-OR Networks

Sep 10, 2019
Xianglei Xing, Tianfu Wu, Song-Chun Zhu, Ying Nian Wu


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Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge

Sep 01, 2019
Zijun Zhang, Linqi Zhou, Liangke Gou, Ying Nian Wu

* 13 pages, 4 figures 

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On Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model

May 27, 2019
Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu


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On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models

Apr 11, 2019
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu


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Learning Trajectory Prediction with Continuous Inverse Optimal Control via Langevin Sampling of Energy-Based Models

Apr 10, 2019
Yifei Xu, Tianyang Zhao, Chris Baker, Yibiao Zhao, Ying Nian Wu


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Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

Apr 09, 2019
Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu


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Multimodal Conditional Learning with Fast Thinking Policy-like Model and Slow Thinking Planner-like Model

Feb 07, 2019
Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu


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Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)

Jan 21, 2019
Quanshi Zhang, Yu Yang, Ying Nian Wu

* In AAAI-19 Workshop on Network Interpretability for Deep Learning. arXiv admin note: substantial text overlap with arXiv:1805.07468 

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Network Transplanting (extended abstract)

Jan 21, 2019
Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu

* In AAAI-19 Workshop on Network Interpretability for Deep Learning. arXiv admin note: substantial text overlap with arXiv:1804.10272 

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Interpretable CNNs

Jan 08, 2019
Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu


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Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model

Dec 28, 2018
Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu


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Learning Dynamic Generator Model by Alternating Back-Propagation Through Time

Dec 27, 2018
Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

* The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI) 2019 
* 10 pages 

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