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Institute for Pure & Applied Mathematics (IPAM) ยท 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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Frank Verstraete: "Mathematical Aspects of Tensor Networks (Part 2/2)"

24 Mar 2021
1h 14m
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Joseph Landsberg: "Introduction to the Geometry of Tensors (Part 2/2)"

24 Mar 2021
1h 13m
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David Gross: "Stabilizer Formalism & Quantum Error Correction Through the Lens of Tensors (Pt 2/2)"

24 Mar 2021
55m
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Anna Seigal: "Tensors in Statistics and Data Analysis"

24 Mar 2021
1h 14m
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Lek-Heng Lim: "What is a tensor? (Part 2/2)"

24 Mar 2021
1h 13m
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Ankur Moitra: "Tensor Decompositions and their Applications (Part 1/2)"

24 Mar 2021
1h 13m
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Frank Verstraete: "Mathematical Aspects of Tensor Networks (Part 1/2)"

24 Mar 2021
1h 23m
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Ming Yuan: "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 2/2)"

24 Mar 2021
1h 16m
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Joseph Landsberg: "Introduction to the Geometry of Tensors (Part 1/2)"

24 Mar 2021
1h 12m
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Lek-Heng Lim: "What is a tensor? (Part 1/2)"

24 Mar 2021
1h 10m
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David Gross: "Stabilizer Formalism & Quantum Error Correction Through the Lens of Tensors (Pt 1/2)"

24 Mar 2021
1h 18m
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Anna Seigal: "From Linear Algebra to Multi-Linear Algebra"

24 Mar 2021
1h 12m
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Ming Yuan: "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 1/2)"

24 Mar 2021
1h 22m
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Sanjeev Arora: "How to allow deep learning on your data without revealing your data"

04 Mar 2021
22m
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Tina Eliassi-Rad: "Task-driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces"

04 Mar 2021
27m
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Louis-Martin Rousseau: "Combining Reinforcement Learning & Constraint Programming for Combinator..."

04 Mar 2021
28m
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Stefanie Jegelka: "Task structure and generalization in graph neural networks"

04 Mar 2021
27m
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Bistra Dilkina: "Decision-focused learning: integrating downstream combinatorics in ML"

04 Mar 2021
27m
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Stefano Gualandi: "Discrete Optimal Transport by Parallel Network Simplex"

04 Mar 2021
29m
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Zico Kolter: "Fast semidefinite programming for (differentiable) combinatorial optimization"

04 Mar 2021
29m
Page 86

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