Multiple-gradient descent algorithm (mgda) for multiobjective opti- mization. Comptes rendus - Mathematique, 1(4867), 2012. DOI: 10.1016/j.crma.2012.03.014.Desideri J-A. Multiple-gradient descent algorithm (MGDA) for multiob- jective optimization. C R Math 2012;350(5-6):313-8....
D\acute{e}sid\acute{e}ri J.-A. Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. Comptes Rendus Mathematique, vol. 350, pp. 313–318, 2012.概本文尝试同时解决 nn 个任务: Ji(θ),i=1,2,⋯,nJi(θ),i=1,2,⋯,n, 其中 θ∈RN,n≤Nθ∈RN,n≤N....
This article compounds and extends several publications in which a Multiple-Gradient Descent Algorithm (MGDA), has been proposed and tested for the treatment of multi-objective differentiable optimization. Originally introduced in [3], the method has bee
有n+1个特征量的gradient descent 特征缩放(feature scaling) 保证多个特征在相似范围内,这样梯度下降法能够更快的收敛 此时代价函数J的等值线图是椭圆形,梯度下降要来回波动,最终才收敛到最小值。 采用特征缩放 除以最大值 0≤x1≤1,0≤x2≤1 此时代价函数J的等值线偏移会变得没那么严重,此时梯度下降法是一条...
多元(多变量)梯度下降与特征缩放、学习率 Gradient Descent for Multiple Variables (Feature Scaling、Learning Rate),程序员大本营,技术文章内容聚合第一站。
其实就是把多变量假设函数带入梯度下降算法之中：梯度运算的使用技巧1：特征缩放（feature scaling）使特征值在一个相近的范围，这样的话更容易收敛从而更快的找到全局最优解。Once again，使特征值在一个相近的范围，从而使梯度下降更快。只要范围相近就OK。归一化（mean normalization）：特征值的范围的...
二、GradientDescentforMultipleVariables—多变量梯度下降上一节我们讨论了多变量(或多特征)线性回归的假设形式,本节介绍...实用技巧,使梯度下降法的运行效果更好。 为了加快梯度下降法的收敛速度,本节中会讲解一种称为特征缩放(featurescaling)的方法。 现在有一个机器学习问题,含有多个特征。你需要做的是确保这些...
二、Gradient descent for multiple variables(多元梯度下降法) (1)Gradient descent for multiple variables 偏导数项展开: (2)Feature Scaling(特征缩放) 原因:若特征规模差别很大(如x1:0-2000,x2:1-5),得到的代价函数可能会不光滑,导致梯度下降收敛速度变慢。
To make sure the gradient descent algorithm can be used to train the model, we employ the reparameterization trick proposed by Kingma and Welling39 to make the optimization function differentiable. Specifically, we don’t directly sample \({{{\bf{z}}}\) from the posterior but first sample \...
which allows the sampled particles to move to the joint high-likelihood region of the target distributions. Interestingly, the asymptotic analysis shows that our approach reduces exactly to the multiple-gradient descent algorithm for multi-objective optimization, as expected. Finally, we conduct comprehen...