2 citations · 2 across the 19 of their papers we have counts for
5 papers · 1 filter
Offline Stochastic Optimization of Black-Box Objective Functions
Juncheng Dong, Zihao Wu, Hamid Jafarkhani +2
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…
Learn2Mix: Training Neural Networks Using Adaptive Data Integration
Shyam Venkatasubramanian, Vahid Tarokh
Accelerating model convergence in resource-constrained environments is essential for fast and efficient neural network training. This work presents learn2mix, a new training strate…
Steinmetz Neural Networks for Complex-Valued Data
Shyam Venkatasubramanian, Ali Pezeshki, Vahid Tarokh
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, r…
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki +2
We present a large-scale dataset called RASPNet for radar adaptive signal processing (RASP) applications to support the development of data-driven models within the adaptive radar…
Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound
Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang +1
In modern radar systems, precise target localization using azimuth and velocity estimation is paramount. Traditional unbiased estimation methods have utilized gradient descent algo…