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DTSTART;TZID=America/New_York:20231101T103000
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DTSTAMP:20260422T180208
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UID:10002806-1698834600-1698838200@cmsa.fas.harvard.edu
SUMMARY:Unveiling Correlated Topological Insulators through Fermionic Tensor Network States
DESCRIPTION:Topological Quantum Matter Seminar \nSpeaker: Shenghan Jiang\, Kavli Institute for Theoretical Sciences UCAS \nTitle: Unveiling Correlated Topological Insulators through Fermionic Tensor Network States \nAbstract: The study of topological band insulators has revealed fascinating phases characterized by band topology indices\, harboring extraordinary boundary modes protected by anomalous symmetry actions. In strongly correlated systems\, it has been established that topological insulator phases persist as stable phases. However\, due to the inability to express the ground states of such systems as Slater determinants\, the formulation of generic variational wavefunctions for numerical simulations is highly desirable.\nIn this talk\, we tackle this challenge by developing a comprehensive framework with fermionic tensor network states. Starting from simple assumptions\, we write down tensor equations\, construct edge theories and extract quantum anomaly data for topological insulators. By exhaustively exploring all possible sets of equations\, we achieve a systematic classification of topological insulator phases. Imposing the solutions of a given set of equations onto local tensors\, we obtain generic variational wavefunctions for corresponding topological insulator phases. Our methodology provides a crucial first step towards simulating topological insulators in strongly correlated systems. \n 
URL:https://cmsa.fas.harvard.edu/event/tqms_11123/
LOCATION:Virtual
CATEGORIES:Topological Quantum Matter Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Topological-Seminar-11.01.23.docx-1.png
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DTSTART;TZID=America/New_York:20231101T153000
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DTSTAMP:20260422T180208
CREATED:20240223T054758Z
LAST-MODIFIED:20240223T054856Z
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SUMMARY:Universality of max-margin classifiers
DESCRIPTION:Probability Seminar \nSpeaker: Youngtak Sohn (MIT) \nTitle: Universality of max-margin classifiers \nAbstract: Many modern learning methods\, such as deep neural networks\, are so complex that they perfectly fit the training data. Despite this\, they generalize well to the unseen data. Motivated by this phenomenon\, we consider high-dimensional binary classification with linearly separable data. First\, we consider Gaussian covariates and characterize linear classification problems for which the minimum norm interpolating prediction rule\, namely the max-margin classification\, has near-optimal prediction accuracy. Then\, we discuss universality of max-margin classification. In particular\, we characterize the prediction accuracy of the non-linear random features model\, a two-layer neural network with random first layer weights. The spectrum of the kernel random matrices plays a crucial role in the analysis. Finally\, we consider the wide-network limit\, where the number of neurons tends to infinity\, and show how non-linear max-margin classification with random features collapse to a linear classifier with a soft-margin objective. \nJoint work with Andrea Montanari\, Feng Ruan\, Jun Yan\, and Basil Saeed.
URL:https://cmsa.fas.harvard.edu/event/probability-11123/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Probability Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Probability-Seminar-11.01.23.png
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