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Items where Person is "Zheng-Stephan"

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Number of items: 11.


Liu, Yukai and Yu, Rose and Zheng, Stephan et al. (2019) NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. . (Submitted)


Zhan, Eric and Zheng, Stephan and Yue, Yisong et al. (2018) Generating Multi-Agent Trajectories using Programmatic Weak Supervision. In: Seventh International Conference on Learning Representations (ICLR 2019), 6-9 May 2019, New Orleans, LA.

Zhan, Eric and Zheng, Stephan and Yue, Yisong et al. (2018) Generative Multi-Agent Behavioral Cloning. . (Submitted)

Dathathri, Sumanth and Zheng, Stephan and Murray, Richard M. et al. (2018) Detecting Adversarial Examples via Neural Fingerprinting. . (Submitted)

Zheng, Stephan and Yu, Rose and Yue, Yisong (2018) Multi-resolution Tensor Learning for Large-Scale Spatial Data. . (Submitted)


Dathathri, Sumanth and Zheng, Stephan and Gao, Sicun et al. (2017) Measuring the Robustness of Neural Networks via Minimal Adversarial Examples. In: Deep Learning: Bridging Theory and Practice, NIPS 2017 workshop, 9 December 2017, Long Beach. CA. (Unpublished)

Yu, Rose and Zheng, Stephan and Anandkumar, Anima et al. (2017) Long-term Forecasting using Tensor-Train RNNs. . (Submitted)

Sha, Long and Lucey, Patrick and Zheng, Stephan et al. (2017) Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories. . (Submitted)

Farrell, Steven and Anderson, Dustin and Calafiura, Paolo et al. (2017) The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking. EPJ Web of Conferences, 150 . Art. No. 00003. ISSN 2100-014X.


Zheng, Stephan and Yue, Yisong and Lucey, Patrick (2016) Generating Long-term Trajectories Using Deep Hierarchical Networks. In: Advances in Neural Information Processing Systems (NIPS 2016). Vol.3. Curran Associates , Red Hook, NY, pp. 1551-1559. ISBN 9781510838819.

Zheng, Stephan and Song, Yang and Leung, Thomas et al. (2016) Improving the Robustness of Deep Neural Networks via Stability Training. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Piscataway, NJ, pp. 4480-4488. ISBN 978-1-4673-8851-1.

This list was generated on Thu Apr 18 08:28:20 2019 PDT.