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Institute for Pure & Applied Mathematics (IPAM) · Science

Institute for Pure & Applied Mathematics (IPAM)

The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.

Episodes

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Joseph Landsberg: "Geometry associated to tensor network states"

25 May 2021
31m
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Anna Keselman: "Spectral Signatures of Quasiparticle Interactions in Antiferromagnets"

25 May 2021
33m
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Mi-Song Dupuy: "A new ordering scheme for QC-DMRG based on symmetry of higher-order singular values"

25 May 2021
28m
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Steven White: "Phase diagram of the 2D t-t'-J model"

25 May 2021
32m
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Caterina De Bacco: "Matrix product states for modeling dynamical processes on networks"

25 May 2021
39m
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Johnnie Gray: "Hyper-optimized tensor network contraction - simplifications, applications & appr..."

06 Apr 2021
32m
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Nick Mayhall: "Selected Configuration Interaction in a Basis of Cluster State Tensor Products"

06 Apr 2021
32m
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Aram Harrow: "Phase transitions in the complexity of simulating random shallow quantum circuits"

06 Apr 2021
31m
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Nithin Govindarajan: "Spline-based separable expansions for approximation, regression & classifi..."

06 Apr 2021
31m
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Ulrich Schollwöck: "Matrix product states for real materials"

06 Apr 2021
29m
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Miles Stoudenmire: "Tensor Networks for Machine Learning and Applications"

06 Apr 2021
31m
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Peter Benner: "Parallel Algorithms for Tensor Train Arithmetic"

06 Apr 2021
37m
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Virginie Ehrlacher: "Tensorized Adaptive Biasing Force method for molecular dynamics simulations"

06 Apr 2021
39m
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Nadav Cohen: "Implicit Regularization in Deep Learning: Lessons Learned from Matrix & Tensor Fac..."

06 Apr 2021
36m
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Lei Wang: "Tropical Tensor Networks"

06 Apr 2021
25m
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Ming Yuan: "Low rank tensor completion"

06 Apr 2021
32m
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Reinhold Schneider: "Solving Backward Stochastic Differential Equation & HJB equations with Tree..."

06 Apr 2021
30m
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Anthony Nouy: "Approximation and learning with tree tensor networks"

06 Apr 2021
31m
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Lars Grasedyck: "Multigrid in Hierarchical Low Rank Tensor Formats"

06 Apr 2021
29m
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Ankur Moitra: "Tensor Decompositions and their Applications (Part 2/2)"

24 Mar 2021
1h 6m
Page 85

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