A geometric look to understand generalization and robustness of deep learning
Guillermo Ortiz Jiménez (Ph.D. Student)
Years of a fierce competition towards the best results have naturally selected the fittest deep learning algorithms. However, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trust, and fairness of modern AI systems. Understanding deep learning is, thus, fundamental for its long-term success; and Guillermo's work is shedding new light on the fundamental mechanisms behind it. By studying the complex interaction between data, architecture, and optimization algorithm, Guillermo is exposing key insights about the way neural networks see the world; and is working towards applying these results to the design of more reliable neural networks. The University of Oxford hosts some of the leading researchers in AI robustness worldwide, so we hope that Guillermo's exchange will result in a fruitful collaboration.
|Primary Host:||Pascal Frossard (EPFL)|
|Exchange Host:||Philip H. S. Torr (University of Oxford)|
|PhD Duration:||01 November 2018 - 01 May 2023|
|Exchange Duration:||01 January 2022 - 30 June 2022 - Ongoing|