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The paper was delivered on arXiv on 26 February 2021. [9] The report (with some details removed, and its appendix cut out to a "Supplementary PDF") was published in Proceedings of the 38th International Conference on Machine Learning, PMLR, [1] which had a submission deadline of February 2021. [10]
Contrastive self-supervised learning uses both positive and negative examples. The loss function in contrastive learning is used to minimize the distance between positive sample pairs, while maximizing the distance between negative sample pairs. [9] An early example uses a pair of 1-dimensional convolutional neural networks to process a pair of ...
Contrastive Hebbian learning is a biologically plausible form of Hebbian learning. It is based on the contrastive divergence algorithm, which has been used to train a variety of energy-based latent variable models. [1] In 2003, contrastive Hebbian learning was shown to be equivalent in power to the backpropagation algorithms commonly used in ...
While traditional linguistic studies had developed comparative methods (comparative linguistics), chiefly to demonstrate family relations between cognate languages, or to illustrate the historical developments of one or more languages, modern contrastive linguistics intends to show in what ways the two respective languages differ, in order to help in the solution of practical problems.
During the 1960s, there was a widespread enthusiasm with this technique, manifested in the contrastive descriptions of several European languages, [1] many of which were sponsored by the Center for Applied Linguistics in Washington, DC. It was expected that once the areas of potential difficulty had been mapped out through contrastive analysis ...
Tone is the use of pitch in language to distinguish lexical or grammatical meaning—that is, to distinguish or to inflect words. [1] All oral languages use pitch to express emotional and other para-linguistic information and to convey emphasis, contrast and other such features in what is called intonation, but not all languages use tones to distinguish words or their inflections, analogously ...
For example, GPT-3, and its precursor GPT-2, [11] are auto-regressive neural language models that contain billions of parameters, BigGAN [12] and VQ-VAE [13] which are used for image generation that can have hundreds of millions of parameters, and Jukebox is a very large generative model for musical audio that contains billions of parameters.
For example, in English, the speech sounds [pʰ] and [b̥] can both occur at the beginning of a word, as in the words pat and bat. Since [pʰ] and [b̥] both occur in the same phonological environment (i.e. at the beginning of a word) but change the meaning of the word they form, they are in contrastive distribution and therefore provide ...