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

Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
doi: https://doi.org/10.1101/622803
Alexander Rives
‡Dept. of Computer Science, New York University, USA
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  • For correspondence: arives@cs.nyu.edu maj@fb.com robfergus@fb.com
Siddharth Goyal
§Facebook AI Research, USA
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Joshua Meier
§Facebook AI Research, USA
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Demi Guo
§Facebook AI Research, USA
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Myle Ott
§Facebook AI Research, USA
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C. Lawrence Zitnick
§Facebook AI Research, USA
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Jerry Ma
§Facebook AI Research, USA
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  • For correspondence: arives@cs.nyu.edu maj@fb.com robfergus@fb.com
Rob Fergus
‡Dept. of Computer Science, New York University, USA
§Facebook AI Research, USA
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  • For correspondence: arives@cs.nyu.edu maj@fb.com robfergus@fb.com
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Posted May 29, 2019.
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Siddharth Goyal, Joshua Meier, 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, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
bioRxiv 622803; doi: https://doi.org/10.1101/622803

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