Gradient descent is an iterative optimization algorithm used for finding the local minimum of a differentiable function. It involves finding the direction in which the function decreases the most and following that direction to minimize the function. ...
The point of all this is that if we start with a guess for our hypothesis and then repeatedly apply these gradient descent equations, our hypothesis will become more and more accurate. So, this is simply gradient descent on the original cost function J. This method looks at every example in...
论文网址:Learning long-term dependencies with gradient descent is difficult 论文一作是图灵奖获得者 Bengio。他本人在访谈中多次提及本论文,发现了 RNN领域梯度爆炸/消失问题。 Abstract Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production ...
Stochastic gradient descent (SGD) runs a training epoch for each example within the dataset and it updates each training example's parameters one at a time. Since you only need to hold one training example, they are easier to store in memory. While these frequent updates can offer more detai...
Gradient Descent : 见:梯度下降(Gradient Descent)小结: 摘自 刘建平Pinard的博客 Description[edit] 1. 梯度 在微积分里面,对多元函数的参数求∂偏导数,把求得的各个参数的偏导数以向量的形式写出来,就是梯度。比如函数f(x,y), 分别对x,y求偏导数,求得的梯度向量就是(∂f/∂x, ∂f/∂y)T,简称...
Gradient descent with adaptive momentum for active contour models. In active contour models (snakes), various vector force fields replacing the gradient of the original external energy in the equations of motion are a popu... G Liu,Z Zhou,H Zhong,... - 《Iet Computer Vision》 被引量: 5...
This paper proposes an online supervised learning algorithm based on gradient descent for multilayer feedforward SNNs, where precisely timed spike trains are used to represent neural information. The online learning rule is derived from the real-time error function and backpropagation mechanism. The ...
To this end, secure and efficient data processing and mining on outsourced private database becomes a primary concern for users. Among different secure data mining and machine learning algorithms, gradient descent method, as a widely used optimization paradigm, aims at approximating a target function...
We develop an Accelerated Distributed Nesterov Gradient Descent (Acc-DNGD) method for convex and smooth objective functions. We show that it achieves a O(1/t1.4-ε) (∀ε ε (0,1.4)) convergence rate when a vanishing step size is used. The convergence rate can be improved to O(1/t2...
This paper introduces the Runge–Kutta Chebyshev descent method (RKCD) for strongly convex optimisation problems. This new algorithm is based on expli