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Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 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 looking up from a word embedding table. [1]
Machine learningand data mining. Neural architecture search ( NAS) [1] [2] is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par or outperform hand-designed architectures. [3] [4] Methods for NAS can be categorized ...
Machine learningand data mining. In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance. [1] It is used in supervised learning and a family of machine learning algorithms that convert weak learners to strong ones. [2]
Multimodal learning, in the context of machine learning, is a type of deep learning using multiple modalities of data, such as text, audio, or images. In contrast, unimodal models can process only one type of data, such as text (typically represented as feature vectors) or images. Multimodal learning is different from combining unimodal models ...
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In ...
Neural machine translation ( NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. It is the dominant approach today [1] : 293 [2] : 1 and can produce translations that rival human translations when ...
Knowledge distillation. In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
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