by instadatahelp | Sep 5, 2023 | AI Blogs
Ridges, Neural Networks, and the Radon Transform By Michael Unser; Volume 24, Issue 37: Pages 1-33, 2023. Abstract A ridge is a function characterized by a one-dimensional profile (activation) and a multidimensional direction vector. Ridges are relevant in neural...
by instadatahelp | Sep 5, 2023 | AI Blogs
Stochastic Optimization under Distributional Drift Authors: Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui; Volume 24, Issue 147, Pages 1-56, 2023. Abstract This study addresses the problem of minimizing a convex function that undergoes unknown and potentially...
by instadatahelp | Sep 5, 2023 | AI Blogs
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems Julián Tachella, Dongdong Chen, Mike Davies; 24(39):1−45, 2023. Abstract Learning the underlying signal model in an ill-posed linear inverse problem is necessary for solving it. However, when the...
by instadatahelp | Sep 5, 2023 | AI Blogs
Fast Convergence of Non-Convex Strongly-Concave Min-Max Problems with PL Condition Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang; 24(148):1−63, 2023. Abstract This paper presents a study on stochastic methods for efficiently solving smooth non-convex...
by instadatahelp | Sep 5, 2023 | AI Blogs
Neural Implicit Flow: A Mesh-Agnostic Dimensionality Reduction Paradigm for Spatio-Temporal Data Shaowu Pan, Steven L. Brunton, J. Nathan Kutz; 24(41):1−60, 2023. Abstract High-dimensional spatio-temporal dynamics often have a low-dimensional subspace representation....
by instadatahelp | Sep 5, 2023 | AI Blogs
Controlling Wasserstein Distances by Kernel Norms in Compressive Statistical Learning In the field of machine learning, comparing probability distributions is a crucial task. Two popular methods for measuring the distance between probability distributions are Maximum...
