Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...

Four-wheeling has never been safer with comprehensive systems like hill descent control. But how does HDC keep you from skidding down the trail?

Abstract: Fractional derivatives generalize integer-order derivatives, making them relevant for studying their convergence in descent-based optimization algorithms. However, existing convergence ...

XRDの一致度をロス関数として勾配降下法で構造同定する手法の妥当性を検証した論文。XRD一致度での最適化はロス関数曲面が不連続で局所解が多く最適化が難しい。対称性を最適化に導入 ...

You might be staring at your SEO checklist in disbelief right now. Rightfully so. You’re already optimized the metadata, headers, internal links, copy, and even done some technical setup for every ...

Abstract: This letter presents a novel stochastic gradient descent algorithm for constrained optimization. The proposed algorithm randomly samples constraints and components of the finite sum ...

ABSTRACT: In this paper, we consider a more general bi-level optimization problem, where the inner objective function is consisted of three convex functions, involving a smooth and two non-smooth ...

A big part of AI and Deep Learning these days is the tuning/optimizing of the algorithms for speed and accuracy. Much of today’s deep learning algorithms involve the use of the gradient descent ...