BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Mahdi Soltanolkotabi (USC) DTSTART:20200708T140000Z DTEND:20200708T150000Z DTSTAMP:20260423T021141Z UID:MADPlus/11 DESCRIPTION:Title: Learning via early stopping and untrained neural nets\nby Mahdi Solta nolkotabi (USC) as part of MAD+\n\n\nAbstract\nModern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data. Such neural networks i n principle have the capacity to (over)fit any set of labels including sig nificantly corrupted ones. Despite this (over)fitting capacity\, over-para meterized networks have an intriguing robustness capability: they are surp risingly robust to label noise when first order methods with early stoppin g are used to train them. Even more surprising\, one can remove noise and corruption from a natural image without using any training data what-so-ev er\, by simply fitting (via gradient descent) a randomly initialized\, ove r-parameterized convolutional generator to a single corrupted image. In th is talk I will first present theoretical results aimed at explaining the r obustness capability of neural networks when trained via early-stopped gra dient descent. I will then present results towards demystifying untrained networks for image reconstruction/restoration tasks such as denoising and those arising in inverse problems such as compressive sensing.\n LOCATION:https://researchseminars.org/talk/MADPlus/11/ END:VEVENT END:VCALENDAR