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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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Wojciech Samek - Concept-Level Explainable AI - IPAM at UCLA

10 Jan 2023
35m
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Osbert Bastani - Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

10 Jan 2023
46m
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Bin Yu - Interpreting Deep Neural Networks towards Trustworthiness - IPAM at UCLA

10 Jan 2023
53m
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Jaesik Choi - Explainable AI to Analyze Internal Decision Mechanism of Deep Neural Networks

10 Jan 2023
54m
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Xiao Fu - Multiview and Self-Supervised Representation Learning: Nonlinear Mixture Identification

09 Jan 2023
48m
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Bolei Zhou - From Network Dissection to Policy Dissection: Emergent Concepts in Deep Representations

09 Jan 2023
44m
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Klaus Robert Muller - AI for the Sciences: Recent Developments for XAI - IPAM at UCLA

09 Jan 2023
46m
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Jamie Haddock - Hierarchical and neural nonnegative tensor factorizations - IPAM at UCLA

02 Dec 2022
50m
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Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA

02 Dec 2022
31m
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Palina Salanevich - STFT Phase retrieval: robustness and generative priors - IPAM at UCLA

02 Dec 2022
47m
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Reinhard Heckel - The role of data and models for deep-learning based image reconstruction

02 Dec 2022
46m
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Mark Iwen - Accurate Recovery of Compactly Supported Smooth Functions from Spectrogram Measurements

02 Dec 2022
51m
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Elizaveta Rebrova - Low-rank tensor recovery from memory-efficient measurements - IPAM at UCLA

01 Dec 2022
49m
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Mary Scott - Supervised and Unsupervised approaches for Electron Microscopy Data Analysis

01 Dec 2022
50m
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Mahdi Soltanolkotabi - Medical image reconstruction via deep learning: architectures, data reduction

01 Dec 2022
45m
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Ben Recht - Splitting the difference between deep and shallow solutions of inverse problems

01 Dec 2022
46m
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Juan Carlos Idrobo - Quantum Phenomena & Electron Microscopy: New Possibilities & Limitations

30 Nov 2022
57m
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Sarah Haigh - Probing atomic behaviour in liquids with STEM : opportunities for machine learning

30 Nov 2022
52m
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Paul Hand - Signal Recovery with Generative Priors - IPAM at UCLA

30 Nov 2022
51m
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Robert Wolkow - Atom-Defined Devices, Ultra-Fast Classical Devices, and Diverse Quantum Devices

30 Nov 2022
49m
Page 58

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