Ashish Vaswani is a machine learning researcher and coauthor of the 2017 paper introducing the Transformer model. The paper's shared-contribution statement credits him with jointly designing and implementing the first Transformer models. It explicitly states that author order is random, so his first position should not be read as sole credit for the architecture.[ref]
Attention Is All You Need by Ashish Vaswani and others. Original byline and contribution footnote
The Transformer model uses self-attention to combine information from different positions in a sequence. In scaled dot-product attention, a query is compared with keys using dot products, then the softmax function supplies weights for combining the corresponding values. The model connects this mechanism to a complete neural network for tasks such as machine translation.[ref]
Attention Is All You Need by Ashish Vaswani and others. Sections3.2–3.2.3: attention computation and its uses
An original one-dimensional example makes the weighting concrete. Choose query , keys , , and values , . With key dimension , the scaled scores are and . The softmax function gives weights and , so the output is . The input values are mixed according to the query–key comparison; this invented example is not a learned translation result.
The original evaluation reported 28.4 BLEU for its large model on the English-to-German newstest2014 test. That is a result under a specific training and evaluation setup, rather than a universal score for all Transformer models.[ref]
Attention Is All You Need by Ashish Vaswani and others. Section6.1 and Table2: big-model translation evaluation
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Ashish Vaswani is a prominent researcher in the field of artificial intelligence and machine learning, particularly known for his contributions to natural language processing (NLP). He is one of the co-authors of the influential paper "Attention is All You Need," which introduced the Transformer model in 2017. This model has since transformed the landscape of NLP and has become the foundation for many state-of-the-art architectures, including BERT, GPT, and others.