In our code,mu,epsilon, andmusare momentum, step size, and coefficientsbetaused for computing running averages of squared gradients in the paper. MomentumRNN We provide implementation of MomentumLSTM, AdamLSTM,
In the construction of PIs, both the variance of model uncertainty and the noise variance must be estimated. From Equation (3), can be obtained as follows: (11) According to Equation (11), the squared residual is calculated as follows: (12) where and can be calculated from Equations ...
The data allow us to identify parameters for the models using Mean squared error (MSE) technique with an algorithm implemented in the MATLAB software. After applying the model for a seven-year period, the obtained results showed that the correlation between the irradiance measurement obtained by ...
The goal of K-means clustering analysis is to minimize the sum of squared errors of the distances between each point and its cluster centre. The basic steps of K-cluster analysis are as follows: Initialization: K data points are randomly selected as initial clustering centres. Assignment: Each...
作者采用sum-squared error为目标函数来优化,增加bounding box loss权重,减少置信度权重。 更重视8维的坐标预测,给这些损失前面赋予更大的loss weight, 记为 λ c o o r d 在pascal VOC训练中取5。对没有object的box的confidence loss,赋予小的loss weight,记为 λ n o o b j 在pascal...
If the mean squared distance Eu2 exceeds a pre-defined threshold, we extract a new boundary point from the set of the previous contour points by taking the maximum of EquationEq. (2). The distance threshold is chosen in dependency of the degree of simplification: The larger this value, the...
The least trimmed squares estimator (LTS) proposed by Rousseeuw8 is given by h minimize (r2)i:n i=1 (13) where (r2)1:n ≤ (r2)2:n ≤···≤ (r2)n:n are the ordered squared residuals. (They are first squared, and then ranked.) The value h plays the same role as in the ...
It is a regression task, for which we use the mean squared error. This is given by: Loss(L)=1NT∑N∑T(ri,t−rˆi,t)2 where T is the number of time periods, and N is the number of stocks. We discuss the different models used to minimize the Loss(L) in the next ...
The ANN predictions for the test cases had much less error than the Ist or 2nd order regression equations (sum of the squared error between measured and predicted values were 17.4, 528.4, and 522.3, respectively). An interactive World Wide Web (www) site has been developed where this ...
"Nearness" is primarily considered in terms of two summary measures for differences between matrices: the Chebychev Norm (maximum element distance) and the Frobenius Norm (sum of squared element distances). Among the existing methods, adapted to function appropriately for actuarial risk matrices, APM...