Results 111 to 120 of about 41,241 (143)
Some of the next articles are maybe not open access.
2015
Markov chains and hidden Markov models (HMMs) are particular types of PGMs that represent dynamic processes. After a brief introduction to Markov chains, this chapter focuses on hidden Markov models. The algorithms for solving the basic problems: evaluation, optimal sequence, and parameter learning are presented.
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Markov chains and hidden Markov models (HMMs) are particular types of PGMs that represent dynamic processes. After a brief introduction to Markov chains, this chapter focuses on hidden Markov models. The algorithms for solving the basic problems: evaluation, optimal sequence, and parameter learning are presented.
openaire +1 more source
Artificial intelligence for natural product drug discovery
Nature Reviews Drug Discovery, 2023Michael W Mullowney +2 more
exaly
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Science, 2020Maziar Raissi +2 more
exaly
Liquid–liquid phase separation drives cellular function and dysfunction in cancer
Nature Reviews Cancer, 2022Sohum Mehta, Jin Zhang
exaly
2019
There are many situations where one must work with sequences. Here is a simple, and classical, example. We see a sequence of words, but the last word is missing. I will use the sequence “I had a glass of red wine with my grilled xxxx.” What is the best guess for the missing word?
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There are many situations where one must work with sequences. Here is a simple, and classical, example. We see a sequence of words, but the last word is missing. I will use the sequence “I had a glass of red wine with my grilled xxxx.” What is the best guess for the missing word?
openaire +1 more source
Transcriptional control of human p53-regulated genes
Nature Reviews Molecular Cell Biology, 2008Eduardo Sontag, Patricia A Chen
exaly