We describe a theoretical method of determining optimal learning rates for on-line gradient descent training of a multilayer neural network (a soft committee machine). A variational approach is used to determine the time-dependent learning rate which maximizes the total decrease in generalization error...
A method for calculating the globally optimal learning rate in on-line gradient-descent training of multilayer neural networks is presented. The method is based on a variational approach which maximizes the decrease in generalization error over a given time frame. We demonstrate the method by ...
In this paper we will try to find optimal parameters for least square regression using gradient descent method. We used concept of least square regression to find error and applied gradient descent on that to find optimal parameters and used positive definite matrix to find whether minimum point ...
For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this ‘Eighty Five Percent Rule’ for artificial neural ...
Monte Carlo policy gradient:使用Monte Carlo方法来估计动作值函数 Baseline:使用一个baseline来减小方差 使用状态值函数来代替动作值函数 7.3.2.1 Monte Carlo policy gradient 我们注意到想利用\nabla_{\theta}J(\theta)=\mathbb{E}_{\pi_\theta}\{q^{\pi_\theta}(s,a)\nabla_\theta\log\pi_\theta(a|...
We consider two classes of proximal-like algorithms for minimizing a proper lower semicontinuous quasi-convex function f(x) subject to non-negative constra... S Pan,JS Chen - 《Journal of Global Optimization》 被引量: 30发表: 2007年 A unifying analysis of projected gradient descent for ℓ_...
Numerous fast algorithms, including the Riemannian gradient descent (RGrad), have been proposed for the TT-format tensor completion. However, the theoretical guarantees of these algorithms are largely missing or sub-optimal, partly due to the complicated and recursive algebraic operations in TT-format...
作者通过修改这两个步骤,设计了动态梯度下降模块(dynamic gradient descent module, DGDM)和层次特征交互模块(hierarchical feature interaction module, HFIM) 动态梯度下降模块(dynamic gradient descent module, DGDM) \mathbf{r}^{(k)}=\mathbf{x}^{(k-1)}-\rho \boldsymbol{\Phi}^{\top}\left(\mathbf{...
Our analysis shows that both LM types converge to their stationary points at a linear rate, but that while prefixLM converges to the optimal solution of linear regression, causalLM convergence dynamics follows that of an online gradient descent algorithm, which is not guaranteed to be optimal ...
Groundwater Level Forecasting Using Artificial Neural Network for Devasugur Nala Watershed in Raichur District, Karnataka (BR), Levenberg-Marquardt (LM) and Gradient Descent with Momentum and Adaptive Learning Rate Back Propagation (GDX) training algorithms were used to train... Anandakumar,BM Babu,...