9 citations · 12 across the 4 of their papers we have counts for
7 papers
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective
Gowthami Somepalli, Liam Fowl, Arpit Bansal +5
We discuss methods for visualizing neural network decision boundaries and decision regions. We use these visualizations to investigate issues related to reproducibility and general…
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk
The rapid recent progress in machine learning (ML) has raised a number of scientific questions that challenge the longstanding dogma of the field. One of the most important riddles…
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding
Veronica Corona, Yehuda Dar, Guy Williams +1
The Magnetic Resonance Imaging (MRI) processing chain starts with a critical acquisition stage that provides raw data for reconstruction of images for medical diagnosis. This flow…
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
Yehuda Dar, Paul Mayer, Lorenzo Luzi +1
We study the linear subspace fitting problem in the overparameterized setting, where the estimated subspace can perfectly interpolate the training examples. Our scope includes the…
Algorithms for Piecewise Constant Signal Approximations
Leif Bergerhoff, Joachim Weickert, Yehuda Dar
We consider the problem of finding optimal piecewise constant approximations of one-dimensional signals. These approximations should consist of a specified number of segments (samp…
Benefiting from Duplicates of Compressed Data: Shift-Based Holographic Compression of Images
Yehuda Dar, Alfred M. Bruckstein
Storage systems often rely on multiple copies of the same compressed data, enabling recovery in case of binary data errors, of course, at the expense of a higher storage cost. In t…