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Exploiting Structure for Scalable and Robust Deep Learning

Citation

Zheng, Stephan Tao (2018) Exploiting Structure for Scalable and Robust Deep Learning. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/4S2Y-CY80. http://resolver.caltech.edu/CaltechThesis:05252018-092016207

Abstract

Deep learning has seen great success training deep neural networks for complex prediction problems, such as large-scale image recognition, short-term time-series forecasting, and learning behavioral models for games with simple dynamics. However, neural networks have a number of weaknesses: 1) they are not sample-efficient and 2) they are often not robust against (adversarial) input perturbations. Hence, it is challenging to train neural networks for problems with exponential complexity, such as multi-agent games, complex long-term spatiotemporal dynamics, or noisy high-resolution image data.

This thesis contributes methods to improve the sample efficiency, expressive power, and robustness of neural networks, by exploiting various forms of low-dimensional structure, such as spatiotemporal hierarchy and multi-agent coordination. We show the effectiveness of this approach in multiple learning paradigms: in both the supervised learning (e.g., imitation learning) and reinforcement learning settings.

First, we introduce hierarchical neural networks that model both short-term actions and long-term goals from data, and can learn human-level behavioral models for spatiotemporal multi-agent games, such as basketball, using imitation learning.

Second, in reinforcement learning, we show that behavioral policies with a hierarchical latent structure can efficiently learn forms of multi-agent coordination, which enables a form of structured exploration for faster learning.

Third, we showcase tensor-train recurrent neural networks that can model high-order mutliplicative structure in dynamical systems (e.g., Lorenz dynamics). We show that this model class gives state-of-the-art long-term forecasting performance with very long time horizons for both simulation and real-world traffic and climate data.

Finally, we demonstrate two methods for neural network robustness: 1) stability training, a form of stochastic data augmentation to make neural networks more robust, and 2) neural fingerprinting, a method that detects adversarial examples by validating the network’s behavior in the neighborhood of any given input.

In sum, this thesis takes a step to enable machine learning for the next scale of problem complexity, such as rich spatiotemporal multi-agent games and large-scale robust predictions.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Machine Learning, Deep Learning, Imitation Learning, Reinforcement Learning, Robust Machine Learning, Algorithms, Big Data, Tensor Learning, Spatiotemporal Data, Multi-agent Systems, Hierarchical Models, Long-term Planning, Structured Exploration
Degree Grantor:California Institute of Technology
Division:Physics, Mathematics and Astronomy
Major Option:Physics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Yue, Yisong
Thesis Committee:
  • Perona, Pietro (chair)
  • Porter, Frank C.
  • Sutskever, Ilya
  • Yue, Yisong
Defense Date:23 April 2018
Non-Caltech Author Email:st.t.zheng (AT) gmail.com
Funders:
Funding AgencyGrant Number
The Powell FoundationUNSPECIFIED
NSFUNSPECIFIED
BloombergUNSPECIFIED
Activision/BlizzardUNSPECIFIED
Northrop Grumman CorporationUNSPECIFIED
Record Number:CaltechThesis:05252018-092016207
Persistent URL:http://resolver.caltech.edu/CaltechThesis:05252018-092016207
DOI:10.7907/4S2Y-CY80
Related URLs:
URLURL TypeDescription
http://www.stephanzheng.comAuthorPersonal Website
https://papers.nips.cc/paper/6520-generating-long-term-trajectories-using-deep-hierarchical-networksPublisherArticle adapted for Ch 2.
https://arxiv.org/abs/1706.07138arXivArticle adapted for Ch 2.
https://arxiv.org/abs/1803.07612arXivArticle adapted for Ch 2.
https://arxiv.org/abs/1711.00073arXivArticle adapted for Ch 3.
https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zheng_Improving_the_Robustness_CVPR_2016_paper.pdfPublisherArticle adapted for Ch 5.
https://arxiv.org/abs/1604.04326arXivArticle adapted for Ch 5.
https://arxiv.org/abs/1803.03870arXivArticle adapted for Ch 5.
ORCID:
AuthorORCID
Zheng, Stephan Tao0000-0002-7271-1616
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10936
Collection:CaltechTHESIS
Deposited By: Stephan Tao Zheng
Deposited On:25 May 2018 18:57
Last Modified:01 Jun 2018 17:55

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