
A Farewell to the BiasVariance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
The rapid recent progress in machine learning (ML) has raised a number o...
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Double Descent and Other Interpolation Phenomena in GANs
We study overparameterization in generative adversarial networks (GANs) ...
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Transfer Learning Can Outperform the True Prior in Double Descent Regularization
We study a fundamental transfer learning process from source to target l...
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Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding
The Magnetic Resonance Imaging (MRI) processing chain starts with a crit...
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Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks
We study the transfer learning process between two linear regression pro...
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Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
We study the linear subspace fitting problem in the overparameterized se...
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Algorithms for Piecewise Constant Signal Approximations
We consider the problem of finding optimal piecewise constant approximat...
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Benefiting from Duplicates of Compressed Data: ShiftBased Holographic Compression of Images
Storage systems often rely on multiple copies of the same compressed dat...
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Compression for Multiple Reconstructions
In this work we propose a method for optimizing the lossy compression fo...
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SystemAware Compression
Many information systems employ lossy compression as a crucial intermedi...
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Optimized PreCompensating Compression
In imaging systems, following acquisition, an image/video is transmitted...
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Yehuda Dar
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