Fault-tolerant ripple-carry binary adder using partial triple modular redundancy (PTMR). - Rahul Parhi; Chris H. Kim; Keshab K. Parhi (ISCAS, 2015)

MP3: A More Efficient Private Presence Protocol. - Rahul Parhi; Michael Schliep; Nicholas Hopper (Financial Cryptography, 2018)

Minimum "Norm" Neural Networks are Splines. - Rahul Parhi; Robert D. Nowak (CoRR, 2019)

Neural Networks, Ridge Splines, and TV Regularization in the Radon Domain. - Rahul Parhi; Robert D. Nowak (CoRR, 2020)

The Role of Neural Network Activation Functions. - Rahul Parhi; Robert D. Nowak (IEEE Signal Process. Lett., 2020)

Banach Space Representer Theorems for Neural Networks and Ridge Splines. - Rahul Parhi; Robert D. Nowak (J. Mach. Learn. Res., 2021)

On Continuous-Domain Inverse Problems with Sparse Superpositions of Decaying Sinusoids as Solutions. - Rahul Parhi; Robert D. Nowak (ICASSP, 2022)

What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory. - Rahul Parhi; Robert D. Nowak (SIAM J. Math. Data Sci., 2022)

Vector-Valued Variation Spaces and Width Bounds for DNNs: Insights on Weight Decay Regularization. - Joseph Shenouda; Rahul Parhi; Kangwook Lee 001; Robert D. Nowak (CoRR, 2023)

Weighted variation spaces and approximation by shallow ReLU networks. - Ronald A. DeVore; Robert D. Nowak; Rahul Parhi; Jonathan W. Siegel (CoRR, 2023)

Banach Space Optimality of Neural Architectures With Multivariate Nonlinearities. - Rahul Parhi; Michael Unser (CoRR, 2023)

The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems. - Rahul Parhi; Michael Unser (IEEE Signal Process. Lett., 2023)

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks. - Rahul Parhi; Robert D. Nowak (IEEE Trans. Inf. Theory, 2023)

Deep Learning Meets Sparse Regularization: A signal processing perspective. - Rahul Parhi; Robert D. Nowak (IEEE Signal Process. Mag., 2023)

Random ReLU Neural Networks as Non-Gaussian Processes. - Rahul Parhi; Pakshal Bohra; Ayoub El Biari; Mehrsa Pourya; Michael Unser (CoRR, 2024)

Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression. - Joseph Shenouda; Rahul Parhi; Kangwook Lee 001; Robert D. Nowak (J. Mach. Learn. Res., 2024)

Distributional Extension and Invertibility of the ( boldsymbol{k} )-Plane Transform and Its Dual. - Rahul Parhi; Michael Unser (SIAM J. Math. Anal., 2024)

A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis. - Akash Kumar 010; Rahul Parhi; Mikhail Belkin (COLT, 2025)

Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities. - Rahul Parhi; Michael Unser (SIAM J. Math. Data Sci., 2025)

Optimal Recovery Meets Minimax Estimation. - Ronald A. DeVore; Robert D. Nowak; Rahul Parhi; Guergana Petrova; Jonathan W. Siegel (CoRR, 2025)

Finding Stable Subnetworks at Initialization with Dataset Distillation. - Luke McDermott; Rahul Parhi (CoRR, 2025)

LoLA: Low-Rank Linear Attention With Sparse Caching. - Luke McDermott; Robert W. Heath Jr.; Rahul Parhi (CoRR, 2025)

Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon. - Tongtong Liang; Dan Qiao; Yu-Xiang Wang; Rahul Parhi (CoRR, 2025)