(gradient descent)与梯度上升(gradient ascent)细节及可视化分析 16:17 [AI 核心概念及计算] 概率计算 01 pytorch 最大似然估计(MLE)伯努利分布的参数 12:35 【矩阵分析】从特征值特征向量到矩阵SVD奇异值分解(np.linalg.svd) 12:03 [矩阵分析] LoRA 矩阵分析基础之 SVD low rank approximation(低秩逼近) 11:...
在这里明确了梯度下降是“找到使函数值最小的那个自变量X”,换句话说,在常见的的分类问题中,就算找到使损失函数(Loss Function)最小的那个参数 θ 。这是梯度下降,找到全局最小值(当然大概率是局部最小值) 在强化学习中,我们没有确切的损失函数,我们无法让损失最小,代替的目标是最大化奖励函数(Reward Function)...
GAN中gradient descent-ascent,收敛性(尤其wT的)无法得以保证,也暗示它需要更复杂的优化算法。 如果有strong convexity(要求了下界的梯度增量;convexity不限定梯度,可以0,可以无穷小),可以得到last iterate的optimality gap,在逐渐趋近于0【TODO: strong convexity和convexity的差距以及该差距对上述理论分析带来的影响】 学...
But if we instead take steps proportional to the positive of the gradient, we approach a local maximum of that function; the procedure is then known as gradient ascent. Gradient descent is generally attributed to Cauchy, who first suggested it in 1847,[1] but its convergence properties for ...
stochastic gradient descent is to minimize cost function: $\theta_j := \theta_j - \alpha \frac{\partial}{\partial \theta_j}J(\theta)$ while gradient ascent is to maximize likelihood function: $\theta_j := \theta_j + \alpha \frac{\partial}{\partial \theta_j}l(\theta)$ 分类: ...
One of the most popular algorithms for solving this problem is the celebrated gradient descent ascent (GDA) algorithm, which has been widely used in machine learning, control theory and economics. Despite the extensive convergence results for the convex-concave setting, GDA with equal stepsize can...
gradient 速记技巧 词源记忆法 来自grade, 级别,层级。用于科学术语。 近义词 ascentn.上升;登高;上坡;追溯; descentn.下降;血统;倾斜; slopen.斜坡;斜面;倾斜;斜率; graden.等级;职别;成绩等级;年级; pitchn.(体育比赛的)场地;程度,顶点;音高;沥青; ...
摘要原文 We study the convergence of Optimistic Gradient Descent Ascent inunconstrained bilinear games. In a first part, we consider the zero-sum caseand extend previous results by Daskalakis et al. in 2018, Liang and Stokes in2019, and others: we prove, for any payoff matrix, the exponential...
In this paper, we propose a novel single-loop stochastic gradient descent-ascent (GDA) algorithm that employs both shuffling schemes and variance reduction to solve nonconvex-strongly concave smooth minimax problems. We show that the proposed algorithm achieves \epsilon -stationarity in expectation in...
Gradient Ascent: In the context of machine learning, gradient descent is more common, where we minimize a loss function. However, in gradient ascent, we aim to maximize an objective function. The idea is similar, but the directions are opposite. Your visualization showcases gradient ascent, with...