However, the derivative of the activation function relu is discontinuous, making it unsuitable for PINNs. The tanh function, with its center point at 0, is often more effective than the sigmoid function (with a center point at 0.5) [26]. Therefore, we choose tanh function as the activation...
The actual mixing process of cells to form a tissue is assumed to be linear and, as such, the relationship between [Math Processing Error]B and [Math Processing Error]S is linear. However, [Math Processing Error]S is unobserved, and the deconvolution algorithm is learned using simulations. ...
However, its small derivative may cause the vanishing gradient problem, so ReLU is more suitable and widely used in deep learning because it has a derivative of one for every positive input. Nevertheless, if the weights in the network always lead to negative inputs into a ReLU neuron, the ...
In [61], the concept of residual learning was introduced by Wei et al. to form a very deep convolutional neural network to make full use of the high nonlinearity of deep learning models. However, most of the existing CNN frameworks directly concatenate the HR-PAN image and LR-MS image ...
The form of information minimization in the IB is not necessary via the dimension reduction or the addition of noise, but it can be via any lossy operation, such as lossy compression or masking. We propose to address the IB principle for MTS via source masking, dimension reduction, and ...
from the audio feature extraction part and the embedding part, and performs feature fusion and selection through a fully connected layer, and outputs the music latent factor vector predicted by the model. The neurons in the fully connected layer all use the ReLU activation function, as shown ...
ConvNeXt, a family of pure ConvNet models introduced by Zhuang et al. [44], achieves competitive accuracy and scalability compared to Transformers while maintaining the simplicity and efficiency of standard ConvNets. Two significant changes in the architecture level are using GELU instead of ReLU an...
Point cloud data, as an important form of three-dimensional data, is widely used in the three-dimensional scanning and reconstruction of cultural relics because it can capture and reconstruct the geometric information of three-dimensional object surfaces with high precision. Point cloud completion, a...
This final network consists of two dense layers with a rectified linear unit (ReLU) as the activation function. The output of the last dense layer was then converted to the probability of each RiPP class by a softmax function. A detailed scheme of the model is shown in Figure S3....
ReLU activations are used. For each game and strategy profile, we train the network for 100 epochs with a batch size of 64. Following the completion of each model training, we compute the utility value using the resulting test CDA and ASR. This utility value represents a single entry in ...