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Tensor Decomposition based Adaptive Model Reduction
Hierarchical Tensor Decompositions in Julia | Frank Otto | JuliaCon 2018
Tensor Decompositions: Uniqueness and Smoothed Analysis
Tensor Decomposition, Sparse Representations and Applications
On the Optimization Landscape of Matrix and Tensor Decomposition Problems
Tensor Decomposition II
6.A.7 Orthogonal decomposition
Tensor Decomposition I
Tamara G. Kolda: Tensor Decomposition
0023 - Tensor Decomposition Techniques for bringing AI to the Edge
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Last Updated: October 2, 2026
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SI(AG)^2 Early Career Prize Lecture: Elina Robeva: Speaker: Elina Robeva (University of British Columbia) Title: NIPS 2016 Spotlight Video - Sublinear Time This video demonstrates an adaptive model reduction approach based on So the next thing that we want to look at is what's called Julia's high-level nature and speed helped me to implement an intricate algorithm for converting a set of high-dimensional data ... Moses Charikar, Princeton University Semidefinite Optimization, Approximation and Applications ... Bernard Mourrain, INRIA Sophia Antipolis Tengyu Ma, Princeton University simons.berkeley.edu/talks/tengyu-ma-10-2-17 Fast Iterative Methods in Optimization. Luke Oeding, Auburn University Algebraic Geometry Boot Camp simons.berkeley.edu/talks/luke-oeding-2014-09-05. JMM 2018: Tamara G. Kolda, Sandia National Laboratories, gives the SIAM Invited Address on " Course: Beginning Arduino Uno Programming in C++ with advanced topics in IoT, Cloud, and Machine Learning Section: ...