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Understanding the implicit regularization imposed by neural network architectures and gradient based optimization methods is a key challenge in deep learning and AI.
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Implicit bias in deep linear classification: Initialization scale vs training accuracy
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Online robust regression via SGD on the
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Implicit regularization in deep learning may not be explainable by norms
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Kernel and rich regimes in overparametrized models
B. Woodworth, S. Gunasekar, J. D. Lee, E. Moroshko, P. Savarese, I. Golan, D. Soudry, and N. Srebro · 2020
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A Mathematical Introduction to Compressive Sensing
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New barriers in complexity theory: On the solvability complexity index and the towers of algorithms
J. Ben-Artzi, A. C. Hansen, O. Nevanlinna, and M. Seidel · 2015
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In search of the real inductive bias: On the role of implicit regularization in deep learning
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On the implicit bias of initialization shape: Beyond infinitesimal mirror descent
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Implicit bias of SGD for diagonal linear networks: a provable benefit of stochasticity
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Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
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Spectra of Jacobi operators via connection coefficient matrices
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Proving existence is not enough: Mathematical paradoxes unravel the limits of neural networks in artificial intelligence
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Computing the sound of the sea in a seashell
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The iterates of the Frank-Wolfe algorithm may not converge
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Curiosities and counterexamples in smooth convex optimization
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WARPd: A linearly convergent first-order primal-dual algorithm for inverse problems with approximate sharpness conditions
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The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and smale’s 18th problem
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L. E. Gazdag and A. C. Hansen · 2022
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Implicit regularization in hierarchical tensor factorization and deep convolutional neural networks
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High-dimensional linear regression via implicit regularization
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B. Adcock, M. J. Colbrook, and M. Neyra-Nesterenko · 2023
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