Rahul Parhi

CoRR, March, 2026

The Inductive Bias of Convolutional Neural Networks: Locality and Weight Sharing Reshape Implicit Regularization.

[BibT_eX]

[DOI]

CoRR, March, 2026

Compositional Function Spaces for Deep Learning.

[BibT_eX]

[DOI]

SIAM Rev., 2026

2025

Towards Sharp Minimax Risk Bounds for Operator Learning.

[BibT_eX]

[DOI]

Ben Adcock

Gregor Maier

CoRR, December, 2025

Generalization Below the Edge of Stability: The Role of Data Geometry.

[BibT_eX]

[DOI]

CoRR, October, 2025

Sharpness of Minima in Deep Matrix Factorization: Exact Expressions.

[BibT_eX]

[DOI]

Anil Kamber

CoRR, September, 2025

Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon.

[BibT_eX]

[DOI]

CoRR, June, 2025

LoLA: Low-Rank Linear Attention With Sparse Caching.

[BibT_eX]

[DOI]

Luke McDermott

Robert W. Heath Jr.

CoRR, May, 2025

Finding Stable Subnetworks at Initialization with Dataset Distillation.

[BibT_eX]

[DOI]

Luke McDermott

CoRR, March, 2025

Optimal Recovery Meets Minimax Estimation.

[BibT_eX]

[DOI]

CoRR, February, 2025

Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., 2025

Random ReLU Neural Networks as Non-Gaussian Processes.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2025

A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis.

[BibT_eX]

[DOI]

Akash Kumar

Mikhail Belkin

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

2024

Distributional Extension and Invertibility of the \(\boldsymbol{k}\)-Plane Transform and Its Dual.

[BibT_eX]

[DOI]

SIAM J. Math. Anal., 2024

Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2024

2023

Deep Learning Meets Sparse Regularization: A signal processing perspective.

[BibT_eX]

[DOI]

IEEE Signal Process. Mag., September, 2023

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, February, 2023

The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 2023

Banach Space Optimality of Neural Architectures With Multivariate Nonlinearities.

[BibT_eX]

[DOI]

CoRR, 2023

Weighted variation spaces and approximation by shallow ReLU networks.

[BibT_eX]

[DOI]

CoRR, 2023

Vector-Valued Variation Spaces and Width Bounds for DNNs: Insights on Weight Decay Regularization.

[BibT_eX]

[DOI]

CoRR, 2023

2022

What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., 2022

On Continuous-Domain Inverse Problems with Sparse Superpositions of Decaying Sinusoids as Solutions.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Banach Space Representer Theorems for Neural Networks and Ridge Splines.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

2020

The Role of Neural Network Activation Functions.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 2020

Neural Networks, Ridge Splines, and TV Regularization in the Radon Domain.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Minimum "Norm" Neural Networks are Splines.

[BibT_eX]

[DOI]

CoRR, 2019

2018

MP3: A More Efficient Private Presence Protocol.

[BibT_eX]

[DOI]

Michael Schliep

Nicholas Hopper

Proceedings of the Financial Cryptography and Data Security, 2018

2015

Fault-tolerant ripple-carry binary adder using partial triple modular redundancy (PTMR).

[BibT_eX]

[DOI]