A Guide to Reproducible Research in Signal Processing and Machine Learning. - Joseph Shenouda; Waheed U. Bajwa (CoRR, 2021)

A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets. - Liu Yang 001; Jifan Zhang; Joseph Shenouda; Dimitris S. Papailiopoulos; Kangwook Lee 001; Robert D. Nowak (CoRR, 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)

A Guide to Computational Reproducibility in Signal Processing and Machine Learning [Tips & Tricks]. - Joseph Shenouda; Waheed U. Bajwa (IEEE Signal Process. Mag., 2023)

ReLUs Are Sufficient for Learning Implicit Neural Representations. - Joseph Shenouda; Yamin Zhou; Robert D. Nowak (ICML, 2024)

A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning. - Julia B. Nakhleh; Joseph Shenouda; Robert D. Nowak (NeurIPS, 2024)

The Effects of Multi-Task Learning on ReLU Neural Network Functions. - Julia B. Nakhleh; Joseph Shenouda; Robert D. Nowak (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)