Probabilistic Inference and Deep Learning

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While neural networks have led to dramatic performance gains in image and language understanding, robotics, and medicine, the algorithms are still sometimes miscalibrated, opaque, and difficult to configure. The proposed research aims to improve the robustness, interpretability, and configurability of neural networks by borrowing techniques from Bayesian statistics and information geometry. The result will be networks which are robust to adversarial inputs and which know what they dont know. This research will also lead to techniques for automatically designing neural network architectures.