Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Jack W. Rae"'
Autor:
Ivan Protsyuk, Martin G. Seneviratne, Andre Saraiva, Natalie Harris, Hugh Montgomery, Mustafa Suleyman, Xavier Glorot, Dominic King, Jack W. Rae, Clifton R. Baker, Alistair Connell, Suman V. Ravuri, Trevor Back, Clemens Meyer, Nenad Tomasev, Harry Askham, Michal Zielinski, Ruth M. Reeves, Joseph R. Ledsam, Shakir Mohamed, Thomas F. Osborne, Cian Hughes, Chris Laing, Alan Karthikesalingam, Valerio Magliulo, Anne Mottram, Christopher Nielson, Sebastien Baur, Julien Cornebise, Demis Hassabis, Geraint Rees
Publikováno v:
Nature Protocols. 16:2765-2787
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron
Autor:
Hugh Montgomery, Alan Karthikesalingam, Xavier Glorot, Christopher Nielson, Harry Askham, Suman V. Ravuri, Trevor Back, Joseph R. Ledsam, Michal Zielinski, Kelly S. Peterson, Geraint Rees, Alistair Connell, Nenad Tomasev, Julien Cornebise, Ivan Protsyuk, Andre Saraiva, Demis Hassabis, Cian Hughes, Chris Laing, Ruth M. Reeves, Shakir Mohamed, Dominic King, Anne Mottram, Jack W. Rae, Mustafa Suleyman, Clemens Meyer, Clifton R. Baker
Publikováno v:
Nature
The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requir
Autor:
Nenad, Tomašev, Natalie, Harris, Sebastien, Baur, Anne, Mottram, Xavier, Glorot, Jack W, Rae, Michal, Zielinski, Harry, Askham, Andre, Saraiva, Valerio, Magliulo, Clemens, Meyer, Suman, Ravuri, Ivan, Protsyuk, Alistair, Connell, Cían O, Hughes, Alan, Karthikesalingam, Julien, Cornebise, Hugh, Montgomery, Geraint, Rees, Chris, Laing, Clifton R, Baker, Thomas F, Osborne, Ruth, Reeves, Demis, Hassabis, Dominic, King, Mustafa, Suleyman, Trevor, Back, Christopher, Nielson, Martin G, Seneviratne, Joseph R, Ledsam, Shakir, Mohamed
Publikováno v:
Nature protocols. 16(6)
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron
Autor:
Ali Razavi, Jack W. Rae
Publikováno v:
ACL
Deep attention models have advanced the modelling of sequential data across many domains. For language modelling in particular, the Transformer-XL -- a Transformer augmented with a long-range memory of past activations -- has been shown to be state-o
Autor:
Pushmeet Kohli, Ray Jiang, Jack W. Rae, Dani Yogatama, Robert Stanforth, Po-Sen Huang, Huan Zhang, Vishal Maini, Johannes Welbl
Publikováno v:
EMNLP (Findings)
Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of generating coherent texts, it also prompts models to internalize social
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b530aab75c46c96120f2006913ca2bd
http://arxiv.org/abs/1911.03064
http://arxiv.org/abs/1911.03064
Autor:
Ivan Protsyuk, Xavier Glorot, Christopher Nielson, Alistair Connell, Cian Hughes, Shakir Mohamed, Chris Laing, Julien Cornebise, Andre Saraiva, Ruth M. Reeves, Demis Hassabis, Alan Karthikesalingam, Hugh Montgomery, Jack W. Rae, Clemens Meyer, Dominic King, Mustafa Suleyman, Suman V. Ravuri, Michal Zielinski, Anne Mottram, Harry Askham, Geraint Rees, Joseph R. Ledsam, Clifton R. Baker, Nenad Tomasev, Kelly S. Peterson, Trevor Back
Early detection of patient deterioration is key to unlocking the potential for targeted preventative care and improving patient outcomes. This protocol describes a workflow for developing deep learning continuous risk models for early prediction of f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ca81db07e34dd4c756362e089da89768
https://doi.org/10.21203/rs.2.10083/v1
https://doi.org/10.21203/rs.2.10083/v1
Autor:
SCOTTI, VINCENZO1 vincenzo.scotti@polimi.it, SBATTELLA, LICIA1 licia.sbattella@polimi.it, TEDESCO, ROBERTO1 roberto.tedesco@polimi.it
Publikováno v:
ACM Computing Surveys. Mar2024, Vol. 56 Issue 3, p1-58. 58p.
Autor:
MIN, BONAN1 bonanmin@amazon.com, ROSS, HAYLEY2 hayleyross@g.harvard.edu, SULEM, ELIOR3 eliors@seas.upenn.edu, BEN VEYSEH, AMIR POURAN4 apouran@cs.uoregon.edu, THIEN HUU NGUYEN4 thien@cs.uoregon.edu, SAINZ, OSCAR5 oscar.sainz@ehu.eus, AGIRRE, ENEKO5 e.agirre@ehu.eus, HEINTZ, ILANA6 ilana@synopticengineering.com, ROTH, DAN3 danroth@seas.upenn.edu
Publikováno v:
ACM Computing Surveys. Feb2024, Vol. 56 Issue 2, p1-40. 40p.
Publikováno v:
ACM Transactions on Information Systems; Jul2024, Vol. 42 Issue 4, p1-60, 60p
Autor:
Schuett, Jonas1,2,3 (AUTHOR) jonas.schuett@governance.ai
Publikováno v:
Law, Innovation & Technology. May2023, Vol. 15 Issue 1, p60-82. 23p.