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In machine learning, grokking, or delayed generalization, is a transition to generalization that occurs many training iterations after the interpolation threshold, after many iterations of seemingly little progress, as opposed to the usual process where generalization occurs slowly and progressively once the interpolation threshold has been ...
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
The areas indicated in the previous section as GBK/1 and GBK/2, taken by themselves, is simply GB 2312-80 in its usual encoding, GBK/1 being the non-hanzi region and GBK/2 the hanzi region. GB 2312, or more properly the EUC-CN encoding thereof, takes a pair of bytes from the range A1 – FE , like any 94² ISO-2022 character set loaded into GR.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.
Two encoding schemes existed for GB 2312: a one-or-two byte 8-bit EUC-CN encoding commonly used, and a 7-bit encoding called HZ [1] for usenet posts. [2]: 94 A traditional variant called GB/T 12345 was published in 1990. The EUC-CN form was later extended into GBK to include all Unicode 1.1 CJK Ideographs in 1993, abandoning the ISO-2022 model.
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods.
In 1943, Warren McCulloch and Walter Pitts proposed the binary artificial neuron as a logical model of biological neural networks. [16] In 1958, Frank Rosenblatt proposed the multilayered perceptron model, consisting of an input layer, a hidden layer with randomized weights that did not learn, and an output layer with learnable connections. [17 ...