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Semantic Scholar is a research tool for scientific literature powered by artificial intelligence. It is developed at the Allen Institute for AI and was publicly released in November 2015. [ 2 ] Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated ...
Paper volumes are printed by the AAAI Press. The Journal for Artificial Intelligence Research (JAIR) is one of the premier publication venues in artificial intelligence. JAIR also stands out in that, since its launch in 1993, it has been 100% open-access and non-profit.
Research papers from more than 55 disciplines Free & Subscription No Elsevier: HAL: Multidisciplinary: 760,000 (2,000,000 metadata) [14] An open-access database for French researchers. Organized into institution and domain portals. Free Yes CNRS's Centre pour la Communication Scientifique Directe (CCSD) RePEc: Research Papers in Economics [15 ...
The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.
Website with academic papers about security topics. This data is not pre-processed Papers per category, papers archive by date. [379] Trendmicro Website with research, news, and perspectives bout security topics. This data is not pre-processed Reviewed list of Trendmicro research, news, and perspectives. [380] The Hacker News
Vicuna LLM is an omnibus Large Language Model used in AI research. [1] Its methodology is to enable the public at large to contrast and compare the accuracy of LLMs "in the wild" (an example of citizen science) and to vote on their output; a question-and-answer chat format is used.
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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.
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