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Transformers were first developed as an improvement over previous architectures for machine translation, [4] [5] but have found many applications since. They are used in large-scale natural language processing, computer vision (vision transformers), reinforcement learning, [6] [7] audio, [8] multimodal learning, robotics, [9] and even playing ...
The name "Transformer" was picked because Jakob Uszkoreit, one of the paper's authors, liked the sound of that word. [9] An early design document was titled "Transformers: Iterative Self-Attention and Processing for Various Tasks", and included an illustration of six characters from the Transformers animated show. The team was named Team ...
A transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was proposed in the 2017 paper "Attention Is All You Need".[1] Text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word ...
Operating on byte-sized tokens, transformers scale poorly as every token must "attend" to every other token leading to O(n 2) scaling laws, as a result, Transformers opt to use subword tokenization to reduce the number of tokens in text, however, this leads to very large vocabulary tables and word embeddings.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1] In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector ...
In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization and activation normalization . Data normalization (or feature scaling ) includes methods that rescale input data so that the features have the same range, mean, variance, or other ...