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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 .
Data-flow analysis is a technique for gathering information about the possible set of values calculated at various points in a computer program.A program's control-flow graph (CFG) is used to determine those parts of a program to which a particular value assigned to a variable might propagate.
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.
In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment.
In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
Utility for proteomics designed to support the preprocessing and analysis of MALDI-TOF mass spectrometry data that loads data from mzML, mzXML and CSV files. It allows users to apply baseline correction, normalization, smoothing, peak detection and peak matching.
Data-flow hardware (see Dataflow architecture) is an alternative to the classic von Neumann architecture. The most obvious example of data-flow programming is the subset known as reactive programming with spreadsheets. As a user enters new values, they are instantly transmitted to the next logical "actor" or formula for calculation.
These tools perform normalization and calculate the abundance of each gene expressed in a sample. [51] RPKM, FPKM and TPMs [52] are some of the units employed to quantification of expression. Some software are also designed to study the variability of genetic expression between samples (differential expression).