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 ...

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 ...

Abstract: A fast gradient-descent (FGD) method is proposed for far-field pattern synthesis of large antenna arrays. Compared with conventional gradient-descent (GD) methods for pattern synthesis where ...

Check the paper on ArXiv: FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification Stochastic gradient-boosted ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

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 ...

Elegantly manage Python virtual environments and project requirements. Poetry gives Python all-in-one management controls akin to what you might enjoy with Go and Rust. A Rust-based solution to a ...