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Weight normalization (WeightNorm) [18] is a technique inspired by BatchNorm that normalizes weight matrices in a neural network, rather than its activations. One example is spectral normalization , which divides weight matrices by their spectral norm .
Another possible reason for the success of batch normalization is that it decouples the length and direction of the weight vectors and thus facilitates better training. By interpreting batch norm as a reparametrization of weight space, it can be shown that the length and the direction of the weights are separated and can thus be trained separately.
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
Weight initialization [ edit ] Kumar suggested that the distribution of initial weights should vary according to activation function used and proposed to initialize the weights in networks with the logistic activation function using a Gaussian distribution with a zero mean and a standard deviation of 3.6/sqrt(N) , where N is the number of ...
This connection is referred to as a "residual connection" in later work. The function () is often represented by matrix multiplication interlaced with activation functions and normalization operations (e.g., batch normalization or layer normalization). As a whole, one of these subnetworks is referred to as a "residual block". [1]
Without normalization, the clusters were arranged along the x-axis, since it is the axis with most of variation. After normalization, the clusters are recovered as expected. In machine learning, we can handle various types of data, e.g. audio signals and pixel values for image data, and this data can include multiple dimensions. Feature ...
A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks.Specifically, a wide variety of network architectures converges to a GP in the infinitely wide limit, in the sense of distribution.
As a result, the dispersity of the reactor lies between that of a batch and that of a homogeneous CSTR. [9] Step growth polymerization is most affected by reactor type. To achieve any high molecular weight polymer, the fractional conversion must exceed 0.99, and the dispersity of this reaction mechanism in a batch or PFR is 2.0.