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Upcoming Seminars

WednesdayApr 21, 202116:30
Algebraic Geometry and Representation Theory Seminar
Speaker:Manish PatnaikTitle:Regularized Eisenstein series on Loop Groups over Function fieldsAbstract:opens in new windowin html    pdfopens in new window
Some time ago, A. Braverman and D. Kazhdan introduced a regularized Eisenstein series on a loop group over a function field and explained how its meromorphic continuation would yield information about automorphic L-functions on finite-dimensional groups (i.e. it would give an extension of Langlands-Shahidi to loop groups). We will give a (new?) construction of these regularized Eisenstein series and then explain what we know about their continuation. This is joint work in progress with S.D. Miller and H. Garland.
ThursdayApr 22, 202112:15
Vision and Robotics Seminar
Speaker:Adi ShamirTitle:A New Theory of Adversarial Examples in Machine LearningAbstract:opens in new windowin html    pdfopens in new windowSchmidt Hall and via Zoom: ** Note that attendance is limited to 50 people under the 'green and purple badges' ** Covid-19 instructions for Schmidt hall: 1. Only vaccinated/recovered people can enter. 2. At least one empty chair is required between each two participants. 3. Sitting is not allowed in the first two rows. 4. All participants should wear a mask (also during the lecture).
The extreme fragility of deep neural networks when presented with tiny perturbations in their inputs was independently discovered by several research groups in 2013. Due to their mysterious properties and major security implications, these adversarial examples had been studied extensively over the last eight years, but in spite of enormous effort they remained a baffling phenomenon with no clear explanation. In particular, it was not clear why a tiny distance away from almost any cat image there are images which are recognized with a very high level of confidence as cars, planes, frogs, horses, or any other desired class, why the adversarial modification which turns a cat into a car does not look like a car at all, and why a network which was adversarially trained with randomly permuted labels (so that it never saw any image which looks like a cat being called a cat) still recognizes most cat images as cats. The goal of this talk is to introduce a new theory of adversarial examples, which we call the Dimpled Manifold Model. It can easily explain in a simple and intuitive way why they exist and why they have all the bizarre properties mentioned above. In addition, it sheds new light on broader issues in machine learning such as what happens to deep neural networks during regular and during adversarial training. Experimental support for this theory, obtained jointly with Oriel Ben Shmuel and Odelia Melamed, will be presented and discussed in the last part of the talk.