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. https://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.

Thesis Files