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.

Institute for Pure & Applied Mathematics (IPAM)

Episodes

Rose Yu: "Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis"

Yuxin Chen: "The Effectiveness of Nonconvex Tensor Completion: Fast Convergence & Uncertainty Qu..."

Matthias Christandl: "Asymptotics of Ranks of Tensors"

Jean Kossaifi: "Efficient Tensor Representation for Deep Learning with TensorLy and PyTorch"

Qing Qu: "Landscape Analysis for Overcomplete Tensor and Neural Collapse"

Babak Hassibi: "Implicit and Explicit Regularization in Deep Neural Networks"

Furong Huang: "Understanding, Interpreting & Designing NN Models Through Tensor Representations"

Shmuel Friedland: "Complexity of Computation of Tensor Rank and Best Rank One Approximation"

Alessandra Bernardi: "On the Dimension of Tensor Network Varieties"

Visu Makam: "Maximum Likelihood Estimation for Tensor Normal Models"

Christian Ikenmeyer: "Minrank: On the Complexity of Orbit Closures"

Anna Seigal: "Principal Components along Quiver Representations"

Aravindan Vijayaraghavan: "Smoothed Analysis for Tensor Decompositions and Unsupervised Learning"

Jiawang Nie: "Learning Diagonal Gaussian Mixture Models and Incomplete Tensor Decompositions"

Madeleine Udell: "Low Rank Tucker Approximation of a Tensor from Streaming Data"

Elina Robeva: "Hidden Variables in Linear Non-Gaussian Causal Models"

Yang Qi: "Inner & outer approximations of tropical polytopes & applications in tropical tensors"

David Pérez-García: "Spectral gap in PEPS"

Nikos Sidiropoulos: "Supervised Learning and Canonical Decomposition of Multivariate Functions"

Anh-Huy Phan: "Chain Tensor Network: Instability and how to deal with it"