Technical - Partner Event

AI Seminar – Ismail Ben Ayed

April 30, 2021 - April 30, 2021
12:00 PM – 1:00 PM MST


Abstract: Despite their unprecedented performances when trained on large-scale labeled data, deep-learning models are seriously challenged when dealing with novel (unseen) classes and limited labeled instances. In contrast, humans can learn new tasks easily from a handful of examples, by leveraging prior experience and context. Few-shot learning attempts to bridge this gap, and has recently triggered substantial research efforts. This talk discusses recent developments in the general, wide-interest subject of learning with limited labels. Specifically, I will discuss state-of-the-art models, which leverage unlabeled data with structural priors, and connect them under a unifying information-theoretic perspective. Furthermore, I will highlight recent results, which point to important limitations of the standard few-shot benchmarks, and question the progress made by an abundant recent few-shot literature, mostly based on complex meta-learning strategies. Classical and simple losses, such as the Shannon entropy or Laplacian regularization, well-established in clustering and semi-supervised learning, achieve outstanding performances.

Presenter Bio: Ismail Ben Ayed is an Associate Professor at Ecole de Technologie Superieure (ETS) Montreal (Université du Québec), where he holds a research Chair on Artificial Intelligence in Medical Imaging. His interests include computer vision, optimization, machine learning and medical imaging.

The University of Alberta Artificial Intelligence (AI) Seminar is a weekly meeting where researchers (including students, developers, and professors) interested in AI can share their current research. Presenters include local speakers from the University of Alberta and industry as well as other institutions. The seminars discuss a wide range of topics related in any way to Artificial Intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems are of interest. Learn more at the AI Seminar website and by subscribing to the mailing list!

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