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Fortran. Abbreviated Test Language for All Systems (ATLAS) is a specialized programming language for use with automatic test equipment (ATE). It is a compiled high-level computer language and can be used on any computer whose supporting software can translate it into the appropriate low-level instructions.
BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1][2] It learned by self-supervised learning to represent text as a sequence of vectors. It had the transformer encoder architecture. It was notable for its dramatic improvement over ...
ATL ( ATLAS Transformation Language) is a model transformation language and toolkit developed and maintained by OBEO and AtlanMod. It was initiated by the AtlanMod team (previously called ATLAS Group). In the field of Model-Driven Engineering (MDE), ATL provides ways to produce a set of target models from a set of source models.
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI. Introduced in 2019, [1][2] T5 models are trained on a massive dataset of text and code using a text-to-text framework. The T5 models are capable of performing the text-based tasks that they were pretrained for.
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
In April 2023, Huawei released a paper detailing the development of PanGu-Σ, a colossal language model featuring 1.085 trillion parameters. Developed within Huawei's MindSpore 5 framework, PanGu-Σ underwent training for over 100 days on a cluster system equipped with 512 Ascend 910 AI accelerator chips, processing 329 billion tokens in more than 40 natural and programming languages.
Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care.
Machine learningand data mining. A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 Transformer. A transformer is a deep learning architecture developed by researchers at Google and based on ...