Joseph Shenouda

According to our database, Joseph Shenouda authored at least 8 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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

Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak

J. Mach. Learn. Res., 2024

The Effects of Multi-Task Learning on ReLU Neural Network Functions.

Julia B. Nakhleh, Joseph Shenouda, Robert D. Nowak

CoRR, 2024

A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning.

Julia B. Nakhleh, Joseph Shenouda, Robert D. Nowak

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

ReLUs Are Sufficient for Learning Implicit Neural Representations.

Joseph Shenouda, Yamin Zhou, Robert D. Nowak

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

A Guide to Computational Reproducibility in Signal Processing and Machine Learning [Tips & Tricks].

Joseph Shenouda, Waheed U. Bajwa

IEEE Signal Process. Mag., March, 2023

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

Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak

CoRR, 2023

2022

A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets.

Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris S. Papailiopoulos, Kangwook Lee, Robert D. Nowak

CoRR, 2022

2021

A Guide to Reproducible Research in Signal Processing and Machine Learning.

Joseph Shenouda, Waheed U. Bajwa

CoRR, 2021