5 citations · 7 across the 4 of their papers we have counts for
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Stochastic Gradient Descent on Nonconvex Functions with General Noise Models
Vivak Patel, Shushu Zhang
Stochastic Gradient Descent (SGD) is a widely deployed optimization procedure throughout data-driven and simulation-driven disciplines, which has drawn a substantial interest in un…
Stochastic Approximation for High-frequency Observations in Data Assimilation
Shushu Zhang, Vivak Patel
With the increasing penetration of high-frequency sensors across a number of biological and physical systems, the abundance of the resulting observations offers opportunities for h…
Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models
Daniel Adrian Maldonado, Vishwas Rao, Mihai Anitescu +1
In this paper we focus on the parameter estimation of dynamic load models with stochastic terms, in particular, load models where protection settings are uncertain, such as in aggr…
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions
Vivak Patel
Stopping criteria for Stochastic Gradient Descent (SGD) methods play important roles from enabling adaptive step size schemes to providing rigor for downstream analyses such as asy…