3 citations · 6 across the 14 of their papers we have counts for
14 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…
Trust, but Verify: Peeling Low-Bit Transformer Networks for Training Monitoring
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Understanding whether deep neural networks are effectively optimized remains challenging, as training occurs in highly nonconvex landscapes and standard metrics provide limited vis…
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…
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…
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…
Deep Learning-Enabled One-Bit DoA Estimation
Farhang Yeganegi, Arian Eamaz, Tara Esmaeilbeig +1
Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling i…
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion
Arian Eamaz, Farhang Yeganegi, Yunqiao Hu +2
This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar…
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion
Arian Eamaz, Farhang Yeganegi, Yunqiao Hu +2
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper in…
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…
Matrix Completion from One-Bit Dither Samples
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with time-varying thres…
HDR Imaging With One-Bit Quantization
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Modulo sampling and dithered one-bit quantization frameworks have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and…
Low-rank Matrix Sensing With Dithered One-Bit Quantization
Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian
We explore the impact of coarse quantization on low-rank matrix sensing in the extreme scenario of dithered one-bit sampling, where the high-resolution measurements are compared wi…
Harnessing the Power of Sample Abundance: Theoretical Guarantees and Algorithms for Accelerated One-Bit Sensing
Arian Eamaz, Farhang Yeganegi, Deanna Needell +1
One-bit quantization with time-varying sampling thresholds (also known as random dithering) has recently found significant utilization potential in statistical signal processing ap…
A New Framework For Spatial Modeling And Synthesis of Genome Sequence
Salman Mohamadi, Farhang Yeganegi, Hamidreza Amindavar
This paper provides a framework in order to statistically model sequences from human genome, which is allowing a formulation to synthesize gene sequences. We start by converting th…