Zobrazeno 1 - 10
of 210
pro vyhledávání: '"PEZESHKI, Ali"'
Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search spaces. Thus, it is essential to leverage existing data to avoid cost
Externí odkaz:
http://arxiv.org/abs/2412.02089
In this work, we introduce a new approach to processing complex-valued data using DNNs consisting of parallel real-valued subnetworks with coupled outputs. Our proposed class of architectures, referred to as Steinmetz Neural Networks, leverages multi
Externí odkaz:
http://arxiv.org/abs/2409.10075
We present a simple performance bound for the greedy scheme in string optimization problems that obtains strong results. Our approach vastly generalizes the group of previously established greedy curvature bounds by Conforti and Cornu\'{e}jols (1984)
Externí odkaz:
http://arxiv.org/abs/2409.05020
Autor:
Venkatasubramanian, Shyam, Kang, Bosung, Pezeshki, Ali, Rangaswamy, Muralidhar, Tarokh, Vahid
This work presents a large-scale dataset for radar adaptive signal processing (RASP) applications, aimed at supporting the development of data-driven models within the radar community. The dataset, called RASPNet, consists of 100 realistic scenarios
Externí odkaz:
http://arxiv.org/abs/2406.09638
The emergence of label-free microscopy techniques has significantly improved our ability to precisely characterize biochemical targets, enabling non-invasive visualization of cellular organelles and tissue organization. Each label-free method has spe
Externí odkaz:
http://arxiv.org/abs/2405.04334
We consider the celebrated bound introduced by Conforti and Cornu\'ejols (1984) for greedy schemes in submodular optimization. The bound assumes a submodular function defined on a collection of sets forming a matroid and is based on greedy curvature.
Externí odkaz:
http://arxiv.org/abs/2404.06669
We study the optimization problem of choosing strings of finite length to maximize string submodular functions on string matroids, which is a broader class of problems than maximizing set submodular functions on set matroids. We provide a lower bound
Externí odkaz:
http://arxiv.org/abs/2308.15758
Imaging beyond the diffraction limit barrier has attracted wide attention due to the ability to resolve image features that were previously hidden. Of the various super-resolution microscopy techniques available, a particularly simple method called s
Externí odkaz:
http://arxiv.org/abs/2305.17348
Autor:
Venkatasubramanian, Shyam, Gogineni, Sandeep, Kang, Bosung, Pezeshki, Ali, Rangaswamy, Muralidhar, Tarokh, Vahid
Recent works exploring data-driven approaches to classical problems in adaptive radar have demonstrated promising results pertaining to the task of radar target localization. Via the use of space-time adaptive processing (STAP) techniques and convolu
Externí odkaz:
http://arxiv.org/abs/2303.08241
We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel. The proposed algorithm recursively yields its estimate of
Externí odkaz:
http://arxiv.org/abs/2211.03903