Leveraging the manifold hypothesis for learning representations in deep neural networks

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Automating intelligent decision taking is easy once starting from the right, high-level representation of relevant information. But sensory input, whether biological (e.g. information relayed by eye and optic nerve, or ear) or electronic (e.g. digital camera or sound recording) start from a very large low-level representation (e.g. millions of pixel intensities) where any potentially relevant information for decision making is hidden, awfully diffuse and scrambled. A fundamental challenge is thus to develop approaches that are able to discover and exploit the hidden statistical regularities in data to learn to produce useful higher level representations from low level ones. In this research program I will leverage a geometric characterization of such statistical regularities in order to develop novel techniques able to extract meaningful representations. Significant progress on this objective could constitute a breakthrough in elucidating one of the key mechanisms of intelligence. It will translate into better artificial perception (vision, audition) systems, lift artificial intelligence capabilities closer to human capabilities, and take to the next level the fundamental science and capabilities of algorithm that underly many modern technological successes such as the Google search engine, the Siri intelligent personal agent, or the self-driving car.