Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Xavier Glorot"'
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1547705a8f9b73ddd1995e08dd7f633d
https://doi.org/10.1038/s41596-021-00513-5
https://doi.org/10.1038/s41596-021-00513-5
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3716eea45fa33fb37c38efb1a54cdd0
https://doi.org/10.1038/s41586-019-1390-1
https://doi.org/10.1038/s41586-019-1390-1
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::b6eee043792243fcb4afed951517d636
https://pubmed.ncbi.nlm.nih.gov/33953393
https://pubmed.ncbi.nlm.nih.gov/33953393
Autor:
Kareem Ayoub, Trevor Back, Rosalind Raine, Alan Karthikesalingam, Pearse A. Keane, Xavier Glorot, Hugh Montgomery, Julien Cornebise, Demis Hassabis, Daniel Visentin, Brendan O'Donoghue, Adnan Tufail, Catherine A Egan, Jeffrey De Fauw, Olaf Ronneberger, Peng T. Khaw, Harry Askham, Reena Chopra, Faith Mackinder, Joseph R. Ledsam, Dominic King, Nenad Tomasev, Julian Hughes, Simon Bouton, Clemens Meyer, George van den Driessche, Cian Hughes, Stanislav Nikolov, Mustafa Suleyman, Balaji Lakshminarayanan, Dawn A Sim, Bernardino Romera-Paredes, Sam Blackwell, Geraint Rees
Publikováno v:
Nature Medicine. 24:1342-1350
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common disease
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45a19e5177294bdc580995357abe336e
https://doi.org/10.1038/s41591-018-0107-6
https://doi.org/10.1038/s41591-018-0107-6
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
Publikováno v:
Machine Learning. 94:233-259
Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::956d0b9f77028642ef549ee957f1d819
https://doi.org/10.1007/s10994-013-5363-6
https://doi.org/10.1007/s10994-013-5363-6
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783319126098
ICPRAM (Selected Papers)
ICPRAM (Selected Papers)
Classifying scenes (e.g. into “street”, “home” or “leisure”) is an important but complicated task nowadays, because images come with variability, ambiguity, and a wide range of illumination or scale conditions. Standard approaches build a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a5799bf1093c039158dc57492d0a482
https://doi.org/10.1007/978-3-319-12610-4_13
https://doi.org/10.1007/978-3-319-12610-4_13
Autor:
Pascal Vincent, Xavier Muller, Salah Rifai, Grégoire Mesnil, Yann N. Dauphin, Yoshua Bengio, Xavier Glorot
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783642237829
ECML/PKDD (2)
ECML/PKDD (2)
We propose a novel regularizer when training an autoencoder for unsupervised feature extraction. We explicitly encourage the latent representation to contract the input space by regularizing the norm of the Jacobian (analytically) and the Hessian (st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22c1a294a3cabe87c999c4c901f67dcc
https://doi.org/10.1007/978-3-642-23783-6_41
https://doi.org/10.1007/978-3-642-23783-6_41