Class imbalance, where datasets often lack sufficient samples for minority classes, is a persistent challenge in machine learning. Existing solutions often generate synthetic data to mitigate this issue, but they typically struggle with complex data distributions, primarily because they focus on oversampli...
47: % the gradient descent update to W1 would be W1 := W1 - alpha * W1grad, and similarly for W2, b1, b2. 48: % 49: %1.forward propagation 50: data_size=size(data); 51: biasPara_1=repmat(b1,1,data_size(2)); 52: biasPara_2=repmat(b2,1,data_size(2)); 53: active_val...
To ensure numerical stability and representative constitutive manifold, a convexity-preserving interpolation scheme tailored to the proposed autoencoder-based data-driven solver is proposed for constructing the material state. In this study, the applicability of the proposed approach is demonstrated by ...
3.通过函数computeNumericalGradient.m计算出大概梯度(EPSILON= 10-4),然后通过函数checkNumericalGradient.m检查上一步写的计算梯度的代码是否正确。首先,通过计算函数 在点[4,10]处的梯度对比用computeNumericalGradient.m中的方法计算该函数的梯度,这两者梯度的差值小于10-9就代表computeNumericalGradient.m中方法是正确...
Jcost = 0.5/m*sum(sum((a3-data).^2)); % compute the weight decay Jweight = 1/2* lambda*sum(sum(W1.^2)) + 1/2*lambda*sum(sum(W2.^2)); % compute the sparse penalty % sparsityParam(rho): The desired average activation for the hidden units ...
[31]. To account for differences in numerical ranges of data modalities we employed batch normalization of hidden units [32], i.e. scaled their inputs to mean zero and standard deviation one. The complementary batch de-normalization was performed at the output layer. Note that batch ...
The methodology was verified on numerical and experimental models based on steel frame structures; and more efficient results are obtained when compared to the traditional ANN methods (Fig. 39). As for future work, the authors stated that the methodology will be expanded to include other modal ...
Sparse Autoencoders Trained on the Same Data Learn Different Features Sparse autoencoders (SAEs) are a useful tool for uncovering human-interpretable features in the activations of large language models (LLMs). While some expect SAEs to find the true underlying features used by a model, our ...
ELECTRONIC data processingPROBLEM solvingENCODINGNeural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data. For unordered multi-categorical variables, existing encoding ...
It is evident that while there are articles that evaluate data augmentation methods for image data, there is a dearth of literature that comprehensively assesses data augmentation techniques for numerical data in the clinical trials field [17,18]. A recent study tried to increase the quantity of ...