(very small) change in the overall weights. Though the brain may use more complicated learning rules, gradient descent is arguably the simplest rule that is effective for general learning and thus a baseline for theorizing about learning in the brain. If gradient descent produces efficient codes,...
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 forrecognition, production or ...
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The next, I guess, time period of your research that you tend to focus on is uncovering the fundamental difficulty of learning in recurrent nets. And I thought that the "Learning Long-Term Dependencies with Gradient Descent is Difficult" was a really interesting paper. I thought it was kind ...
指出了 RNN 所面临的问题: temporal contingencies present in the input/output sequences span intervals ,也就是所谓的长依赖问题(long-term dependencies)。接下来指出问题的原因是基于梯度的训练方法。这种方法中存在 trade-off bbetween efficient learning by gradient descent and latching on information for long...
Gradient descent is, in fact, a general-purpose optimization technique that can be applied whenever the objective function is differentiable. Actually, it turns out that it can even be applied in cases where the objective function is not completely differentiable through use of a device called...
1.2 Stochastic gradient descent 这个方法叫做随机梯度下降,简称SGD。该方法是为一个样例(样例包含训练样本 和标注 )来更新一次参数,如下式所示: 因为该更新方法是对每一个样例而言的,所以参数更新比Batch的方式快。但这种方式可能会导致参数更新波动较大,如下图所示。
简介:【深度学习系列】(二)--An overview of gradient descent optimization algorithms 一、摘要 梯度下降优化算法虽然越来越流行,但经常被用作黑盒优化器,因为很难找到对其优缺点的实际解释。本文旨在为读者提供有关不同算法行为的直观信息,使他们能够使用这些算法。在本概述过程中,我们将介绍梯度下降的不同变体,总结...
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深度学习的架构和最新发展,包括CNN、RNN、造出无数假脸的GAN,都离不开梯度下降算法。 梯度可以理解成山坡上某一点上升最快的方向,它的反方向就是下降最快的方向。想要以最快的方式下山,就沿着梯度的反方向走。 看起来像沙盘推演的东西,其实是我们撒出的小球,它们会沿着梯度下降的方向滚到谷底。 而梯度下降算法...