Generalization bounds for rational self-supervised learning algorithms, or “Understanding generalizations requires rethinking deep learning”
https://youtu.be/aVB1qFPeEmo Speakers: Boaz Barak and Yamini Bansal, Harvard University Dept. of Computer Science Title: Generalization bounds for rational self-supervised learning algorithms, or "Understanding generalizations requires rethinking deep learning" Abstract: The generalization gap of a learning algorithm is the expected difference between its performance on the training data and its performance on fresh unseen test samples. […]
Some exactly solvable models for machine learning via Statistical physics
Virtualhttps://youtu.be/uUUeTYzMu0Q Speaker: Florent Krzakala, EPFL Title: Some exactly solvable models for machine learning via Statistical physics Abstract: The increasing dimensionality of data in the modern machine learning age presents new challenges and opportunities. The high dimensional settings allow one to use powerful asymptotic methods from probability theory and statistical physics to obtain precise characterizations and […]