Using error decay prediction to overcome practical issues of deep active learning for named entity recognition

Autor: Shankar Vembu, Haw-Shiuan Chang, Andrew McCallum, Sunil Mohan, Rheeya Uppaal

Publikováno v: Machine Learning. 109:1749-1778

Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, including (a) inability to use uncertainty sampling with black-box models, (b)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6bb3e81a1f14db9fe85a08406b086f3
https://doi.org/10.1007/s10994-020-05897-1

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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Zechen, Chong, Oliver, Drechsel, Lewis Jonathan Dursi, Roland, Eils, Shadrielle M, G Espiritu, Robert, S Fulton, Shengjie, Gao, Josep L, L Gelpi, Mark, B Gerstein, Santiago, Gonzalez, Ivo, G Gut, Faraz, Hach, Michael, C Heinold, Jonathan, Hinton, Taobo, Hu, Vincent, Huang, Huang, Yi, Barbara, Hutter, David, R Jones, Jongsun, Jung, Natalie, Jäger, Hyung-Lae, Kim, Kortine, Kleinheinz, Sushant, Kumar, Yogesh, Kumar, Christopher, M Lalansingh, Ivica, Letunic, Dimitri, Livitz, Eric, Z Ma, Yosef, E Maruvka, R Jay Mashl, Andrew, Menzies, Ana, Milovanovic, Morten Muhlig Nielsen, Stephan, Ossowski, Nagarajan, Paramasivam, Jakob Skou Pedersen, Marc, D Perry, Montserrat, Puiggròs, Keiran, M Raine, Esther, Rheinbay, Romina, Royo, S Cenk Sahinalp, Iman, Sarrafi, Matthias, Schlesner, Jared, T Simpson, Lucy, Stebbings, Miranda, D Stobbe, Jon, W Teague, Grace, Tiao, David, Torrents, Jeremiah, A Wala, Jiayin, Wang, Sebastian, M Waszak, Joachim, Weischenfeldt, Michael, C Wendl, Johannes, Werner, 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Publikováno v: Nature Communications, 11(1):4748. Nature Publishing Group
Nature communications, 11(1):4748. Nature Publishing Group
Nature communications, 11 (1
NATURE COMMUNICATIONS
Nature Communications, 11 (1)
Nature Communications, Vol 11, Iss 1, Pp 1-27 (2020)
Bailey, M H, Meyerson, W U, Dursi, L J, Wang, L B, Dong, G, Liang, W W, Weerasinghe, A, Li, S, Kelso, S, Akbani, R, Anur, P, Bailey, M H, Buchanan, A, Chiotti, K, Covington, K, Creason, A, Ding, L, Weischenfeldt, J, Brunak, S, Ding, L, Favero, F, MC3 Working Group, PCAWG novel somatic mutation calling methods working group & PCAWG Consortium 2020, ' Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples ', Nature Communications, vol. 11, no. 1, 4748 . https://doi.org/10.1038/s41467-020-18151-y
Bailey, Matthew H; Meyerson, William U; Dursi, Lewis Jonathan; Wang, Liang-Bo; Dong, Guanlan; Liang, Wen-Wei; Weerasinghe, Amila; Li, Shantao; Kelso, Sean; Saksena, Gordon; Ellrott, Kyle; Wendl, Michael C; Wheeler, David A; Getz, Gad; Simpson, Jared T; Gerstein, Mark B; Ding, Li (2020). Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples. Nature Communications, 11(1), p. 4748. Springer Nature 10.1038/s41467-020-18151-y
Nature communications, vol 11, iss 1
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RUO. Repositorio Institucional de la Universidad de Oviedo
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Dipòsit Digital de la UB
Universidad de Barcelona
Nature Communications, 11, 1
Recercat. Dipósit de la Recerca de Catalunya
Digital.CSIC. Repositorio Institucional del CSIC
Nature Communications, 11
RUO: Repositorio Institucional de la Universidad de Oviedo
Universidad de Oviedo (UNIOVI)
Bailey, M H, Meyerson, W U, Dursi, L J, Wang, L B, Dong, G, Liang, W W, Weerasinghe, A, Li, S, Kelso, S, Akbani, R, Anur, P, Bailey, M H, Buchanan, A, Chiotti, K, Covington, K, Creason, A, Ding, L, MC3 Working Group, PCAWG novel somatic mutation calling methods working group & PCAWG Consortium 2020, ' Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples ', Nature Communications, vol. 11, no. 1, 4748 . https://doi.org/10.1038/s41467-020-18151-y
Recercat: Dipósit de la Recerca de Catalunya
Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79caf69aa08507159a37977adcb4937b
https://www.bib.irb.hr/1104730

Beam search algorithms for multilabel learning

Autor: Shankar Vembu, Charles Elkan, Abhishek Kumar, Aditya Krishna Menon

Publikováno v: Machine Learning. 92:65-89

Multilabel learning is a machine learning task that is important for applications, but challenging. A recent method for multilabel learning called probabilistic classifier chains (PCCs) has several appealing properties. However, PCCs suffer from the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c37312ba46a6dd7a926605b8c78de7e0
https://doi.org/10.1007/s10994-013-5371-6

Interactive Learning from Multiple Noisy Labels

Autor: Sandra Zilles, Shankar Vembu

Publikováno v: Machine Learning and Knowledge Discovery in Databases ISBN: 9783319461274
ECML/PKDD (1)

Interactive learning is a process in which a machine learning algorithm is provided with meaningful, well-chosen examples as opposed to randomly chosen examples typical in standard supervised learning. In this paper, we propose a new method for inter

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d827c00fa00c60e7785d4d82318da44b
https://doi.org/10.1007/978-3-319-46128-1_31

Chemical gas sensor drift compensation using classifier ensembles

Autor: Shankar Vembu, Margie L. Homer, Tuba Ayhan, Margaret A. K. Ryan, Alexander Vergara, Ramon Huerta

Publikováno v: Sensors and Actuators B: Chemical. :320-329

Sensor drift remains to be the most challenging problem in chemical sensing. To address this problem we have collected an extensive dataset for six different volatile organic compounds over a period of three years under tightly controlled operating c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6d13f32ceef17327e7b688371feeed9c
https://doi.org/10.1016/j.snb.2012.01.074