6 papers
Training Under Challenge: Executable Certificates and Challenge-Closed Optimality for Neural Networks
Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian
A flat training curve does not reveal whether a neural network has reached a global optimum, is locally trapped, is representation-limited, or is mismatched to its trainer. We intr…
Fast and Robust LRSD-based SAR/ISAR Imaging and Decomposition
Hamid Reza Hashempour, Majid Moradikia, Hamed Bastami +2
The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary synthetic aperture radar (SAR) imaging have shown significant improvement in the reconstr…
From Quasi-Isometric Embeddings to Finite-Volume Property: A Theoretical Framework for Quantized Matrix Completion
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
We delve into the impact of memoryless scalar quantization on matrix completion. Our primary motivation for this research is to evaluate the recovery performance of nuclear norm mi…
Physics-Inspired Binary Neural Networks: Interpretable Compression with Theoretical Guarantees
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Why rely on dense neural networks and then blindly sparsify them when prior knowledge about the problem structure is already available? Many inverse problems admit algorithm-unroll…
Sample Abundance for Signal Processing: A Brief Introduction
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
This paper reports, by way of introduction, on the advances made by our group and the broader signal processing community on the concept of sample abundance; a phenomenon that natu…
Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning
Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian
Deep learning models excel at capturing complex representations through sequential layers of linear and non-linear transformations, yet their inherent black-box nature and multi-mo…