We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attention in recent years ...
TSFitPy is a pipeline designed to determine stellar abundances and atmospheric parameters through the use of Nelder-Mead (simplex algorithm) minimization. It calculates model spectra "on the fly" ...
Abstract: The Nelder-Mead simplex method is a well-known algorithm enabling the minimization of functions that are not available in closed-form and that need not be differentiable or convex.
Formal optimization is nowadays ubiquitous in microwave design. It is frequently conducted using electromagnetic (EM) simulations, which guarantee dependability. Yet, it is computationally expensive.
Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...
Dipartimento di Farmacia, Università degli Studi di Napoli “Federico II”, Via D. Montesano 49, 80131 Naples, Italy ...
Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature ...
Adaptive structures have great potential to meet the growing demand for energy efficiency in buildings and engineering structures. While some structures adapt to varying loads by a small change in ...
Center on Stochastic Modeling, Optimization, and Statistics (COSMOS), The University of Texas at Arlington, Arlington, TX, USA. Quantitative decision analysis involves notions of comparison and ...
The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce ...
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