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Explores the application of machine learning in medicine, emphasizing interpretability, variability between patients, and the quest for transparent equations in medical models.
Explores decoding from neural models in modern NLP, covering encoder-decoder models, decoding algorithms, issues with argmax decoding, and the impact of beam size.
Explores the theoretical properties and practical power of Recurrent Neural Networks, including their relationship to state machines and Turing completeness.
