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 dec
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 ...
For large scale learning problems, it is desirable if we can obtain the optimal model parameters by going through the data in only one pass. Polyak and Juditsky (1992) showed that asymptotically the test performance of the simple average of the parameters obtained by stochastic gradient descent ...
这其中SGD(Stochastic Gradient Descent)是最常见的一种。最基本的SGD一次只使用一个样本来计算梯度,但是这样会有很大的随机性。为了在计算效率和稳定性之间取得平衡,我们可以使用mini-batch SGD。mini-batch SGD为了取得良好的效果,需要对几个参数进行调参,比如batch size、learning rate等。但是,SGD容易陷入局部最优...
We have designed a global optimization technique for mirror reflection learning strategy, which explores new search spaces by generating reflection points based on the current solution. In GEO-DLS, this strategy is cleverly applied to the cruising and attacking behavior of the Golden Eagle, thereby ...
More generally, for arbitrary noise spectrum, the optimization is implemented using stochastic gradient descent (SGD) algorithms. We implement the SGD using the Adam optimization algorithm implemented in TensorFlow44,45. Note that it is also straightforward to add additional constraints on the variables...
cationof the averaging step suff i ces to recover the O(1/T) rate, and no other change ofthe algorithm is necessary. We also present experimental results which supportour f i ndings, and point out open problems.1 IntroductionStochastic gradient descent (SGD) is one of the simplest and mo...
In this paper, we propose a novel approach for unsupervised domain adaptation that relates notions of optimal transport, learning probability measures, and
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{...
Algorithmic reproducibility measures the deviation in outputs of machine learning algorithms upon minor changes in the training process. Previous work suggests that first-order methods would need to trade-off convergence rate (gradient complexity) for better reproducibility. In this work, we challenge ...