Adaptive cubic regularization (ARC) methods for unconstrained optimization compute steps from linear systems involving a shifted Hessian in the spirit of the Levenberg-Marquardt and trust-region methods. The standard approach consists in performing an iterative search for the shift akin to solving the ...
L. Toint, Adaptive Cubic Regularisation Methods for Unconstrained Optimization. Part I: Motivation, Convergence and Numerical Results... C Cartis,NIM Gould,PL Toint - 《Mathematical Programming》 被引量: 250发表: 2011年 Evaluation complexity of adaptive cubic regularization methods for convex ...
L. Toint. An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity. IMA J. Numer... C Cartis,NIM Gould,PL Toint - 《Ima Journal of Numerical Analysis》 被引量: 98发表: 2012年 A computationally efficient robust adaptive...
273 instead of 403 in the previous section. This dataset represents face-centred cubic (FCC) compositions in the 7-element CoCrFeMnNiV-Al high entropy alloy (HEA) space. Specifically, we focus on the task of exploring the stacking fault energy (SFE)...
We achieve this by merging the ideas of cubic regularization with a certain adaptive Levenberg-Marquardt penalty. In particular, we show that the iterates... K Mishchenko - 《Siam Journal on Optimization A Publication of the Society for Industrial & Applied Mathematics》 被引量: 0发表: 2023年...
§1.2自适应线性(adaptive linear)感知器 §1.3 madaline网络 §1.4 bp网络 §1.5 bp网络的应用 习题 第二章 联想记忆神经 … product.china-pub.com|基于2个网页 3. 线性适配倒角 我有我的个性~`~忽忽 ... Normal bevel 法线倒角Adaptive linear线性适配倒角Adaptive cubic 立方适配倒角 ... ...
To continuously visualize outcome variation according to cycle phase without imposing discrete phases such as menstruation and the fertile window, we fit Bayesian mixed models with a Gaussian family and cyclic cubic splines over backward-counted cycle days by HC status. For slight regularization, we ...
Bi-cubic, spatially-adaptive regularization [1–6], edge guided interpolation [7–11], transform-domain methods [12–15], multi-scale geometry analysis [16–19], neural network [20–23], fractional Brownian motion model [24], nonlinear partial differential equation [25], Bayesian estimator [...
Knowledge distillation is a type of regularization acting on the loss function: Sheet-metalNet uses the following multi-class cross-entropy loss function to encourage the output vector \(\hat{y}\) to be consistent with the ground truth y: $$\begin{aligned} L_{hard}\left( y,\hat{y}\rig...
Also considered were safeguards usually undertaken to avoid overfitting, including regularization as well as the use of ensemble models. Regarding the former, the hyperparameters were optimized using cross validation and for the latter, bagging as well as boosting were used (see Supplemental Fig. 3 ...