Browsing by Subject "MACHINE LEARNING"
Now showing items 1-2 of 2
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Assessment of methods for predicting eukaryotic promoter sequences
(IEEE, 2023)Identifying promoters is challenging due to their short sequences, low conservation, and complex regulation. Historically, this was done through slow and expensive experimental methods. Efficient pattern recognition and ... -
Exploring the effects of silent data corruption in distributed deep learning training
(Institute of Electrical and Electronics Engineers (IEEE), 2022-11-02)The profound impact of recent developments in artificial intelligence is unquestionable. The applications of deep learning models are everywhere, from advanced natural language processing to highly accurate prediction of ...