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The first two population distribution parameters and are usually characterized as location and scale parameters, while the remaining parameter(s), if any, are characterized as shape parameters, e.g. skewness and kurtosis parameters, although the model may be applied more generally to the parameters of any population distribution with up to four ...
Also known as min-max scaling or min-max normalization, rescaling is the simplest method and consists in rescaling the range of features to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data. The general formula for a min-max of [0, 1] is given as: [3]
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of n {\textstyle n} objects in a set into a configuration of n {\textstyle n} points mapped into an abstract Cartesian space .
It is named "chinchilla" because it is a further development over a previous model family named Gopher. Both model families were trained in order to investigate the scaling laws of large language models. [2] It claimed to outperform GPT-3. It considerably simplifies downstream utilization because it requires much less computer power for ...
Correspondence analysis (CA) is a multivariate statistical technique proposed [1] by Herman Otto Hartley (Hirschfeld) [2] and later developed by Jean-Paul Benzécri. [3] It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data.
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes.The method was invented by John Platt in the context of support vector machines, [1] replacing an earlier method by Vapnik, but can be applied to other classification models. [2]
The Chinchilla scaling law analysis for training transformer language models suggests that for a given training compute budget (), to achieve the minimal pretraining loss for that budget, the number of model parameters and the number of training tokens should be scaled in equal proportions, (), ().
Random projection is computationally simple: form the random matrix "R" and project the data matrix X onto K dimensions of order (). If the data matrix X is sparse with about c nonzero entries per column, then the complexity of this operation is of order O ( c k N ) {\displaystyle O(ckN)} .