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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.
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.
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
The AI technology is designed to identify hidden connections and links between research topics. [14] Like the previously cited search engines, Semantic Scholar also exploits graph structures, which include the Microsoft Academic Knowledge Graph , Springer Nature's SciGraph , and the Semantic Scholar Corpus (originally a 45 million papers corpus ...
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
This paper's goal was to improve upon 2014 seq2seq technology, [11] and was based mainly on the attention mechanism developed by Bahdanau et al. in 2014. [12] The following year in 2018, BERT was introduced and quickly became "ubiquitous". [13] Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model.
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This research investigates a novel approach to language modeling, MambaByte, which departs from the standard token-based methods. Unlike traditional models that rely on breaking text into discrete units, MambaByte directly processes raw byte sequences. This eliminates the need for tokenization, potentially offering several advantages: [8]
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