ABSTRACT: Bipolar disorder (BD) affects approximately 45 million individuals worldwide and is characterized by recurrent episodes of mania, hypomania, and depression, with an average diagnostic delay ...

Nhat A. Nghiem, State University of New York at Stony Brook, and Tzu-Chieh Wei have significantly enhanced quantum algorithms for core computational problems, moving beyond initial applications like ...

Gradient descent has a fundamental limitation: on most real-world loss surfaces, it is inefficient. When the surface has uneven curvature—steep in one direction and flat in another, which is common in ...

Abstract: We propose an adaptive moment estimation (Adam)-based 2 nd-order Volterra nonlinear equalizer (VNLE) employing a mini-batch gradient descent (MGD) algorithm for intensity ...

As modern computing becomes limited by energy consumption, there is growing interest in physical computing paradigms that can operate closer to fundamental thermodynamic limits. Thermodynamic ...

In this study, we focus on investigating a nonsmooth convex optimization problem involving the l 1-norm under a non-negative constraint, with the goal of developing an inverse-problem solver for image ...

Every data science interview eventually arrives at the same question: "How does gradient boosting actually work?" You can say "it builds trees sequentially" and watch the interviewer nod politely, or ...

Gradient boosting builds accurate predictions by stacking small corrections on top of each other. The first model guesses the average house price. The second model looks at the leftover errors and ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...