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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
doi: https://doi.org/10.1101/622803
Alexander Rives
1Facebook AI Research
2Dept. of Computer Science, New York University
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  • For correspondence: arives@cs.nyu.edu
Joshua Meier
1Facebook AI Research
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Tom Sercu
1Facebook AI Research
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Siddharth Goyal
1Facebook AI Research
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Zeming Lin
2Dept. of Computer Science, New York University
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Jason Liu
1Facebook AI Research
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Demi Guo
3Harvard University
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Myle Ott
1Facebook AI Research
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C. Lawrence Zitnick
1Facebook AI Research
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Jerry Ma
4Booth School of Business, University of Chicago & Yale Law School
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Rob Fergus
2Dept. of Computer Science, New York University
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Posted December 15, 2020.
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
bioRxiv 622803; doi: https://doi.org/10.1101/622803
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
bioRxiv 622803; doi: https://doi.org/10.1101/622803

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