Zobrazeno 1 - 10
of 20
pro vyhledávání: '"George E. Dahl"'
Autor:
Ryan G. Gomes, Bellington Vwalika, Chace Lee, Angelica Willis, Marcin Sieniek, Joan T. Price, Christina Chen, Margaret P. Kasaro, James A. Taylor, Elizabeth M. Stringer, Scott Mayer McKinney, Ntazana Sindano, George E. Dahl, William Goodnight, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T. Saensuksopa, Kris Liu, Tiya Tiyasirichokchai, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty
Publikováno v:
Communications Medicine, Vol 2, Iss 1, Pp 1-9 (2022)
Gomes et al. develop machine learning models for gestational age and fetal malpresentation assessment on fetal ultrasound. The authors optimize their system for use in low-resource settings, using novice ultrasound operators, simplified imaging proto
Externí odkaz:
https://doaj.org/article/702c5b580ff74854b77fdd8e9ee6e8e6
Autor:
Ali Bashir, Qin Yang, Jinpeng Wang, Stephan Hoyer, Wenchuan Chou, Cory McLean, Geoff Davis, Qiang Gong, Zan Armstrong, Junghoon Jang, Hui Kang, Annalisa Pawlosky, Alexander Scott, George E. Dahl, Marc Berndl, Michelle Dimon, B. Scott Ferguson
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without signif
Externí odkaz:
https://doaj.org/article/9508da0079cf405d9f6f644c1119f62f
Autor:
George E. Dahl, Samuel R. Bowman
Publikováno v:
NAACL-HLT
Evaluation for many natural language understanding (NLU) tasks is broken: Unreliable and biased systems score so highly on standard benchmarks that there is little room for researchers who develop better systems to demonstrate their improvements. The
Publikováno v:
Machine Learning Meets Quantum Physics ISBN: 9783030402440
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been descr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::72eb61489e30169af7c5d54f9bf45702
https://doi.org/10.1007/978-3-030-40245-7_10
https://doi.org/10.1007/978-3-030-40245-7_10
Autor:
Niels Olson, George E. Dahl, Martin C. Stumpe, Timo Kohlberger, Mohammad Norouzi, Jenny L. Smith, Jason D. Hipp, Arash Mohtashamian, Lily Peng, Yun Liu
Publikováno v:
Archives of pathologylaboratory medicine. 143(7)
Context.—Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci.Objective.—To evaluate t
Autor:
George E. Dahl, Cory Y. McLean, Lizzie Dorfman, David Alexander, Mark A. DePristo, Akosua Busia, Pi-Chuan Chang, Ryan Poplin, Clara Fannjiang
MotivationInferring properties of biological sequences--such as determining the species-of-origin of a DNA sequence or the function of an amino-acid sequence--is a core task in many bioinformatics applications. These tasks are often solved using stri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59cc945d98862171a4f1a754be0b052e
https://doi.org/10.1101/353474
https://doi.org/10.1101/353474
Publikováno v:
TextGraphs@NAACL-HLT
Natural language text exhibits hierarchical structure in a variety of respects. Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data. Recent work by Nickel & Kie
Publikováno v:
EMNLP
Neural language models are a critical component of state-of-the-art systems for machine translation, summarization, audio transcription, and other tasks. These language models are almost universally autoregressive in nature, generating sentences one
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95fbd1bee93e084e8a2458f903cfe209
Autor:
Luke Hutchison, Oriol Vinyals, Felix A. Faber, Steven Kearnes, George E. Dahl, Patrick Riley, Justin Gilmer, O. Anatole von Lilienfeld, Samuel S. Schoenholz, Bing Huang
Publikováno v:
Journal of chemical theory and computation. 13(11)
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of 13 electronic ground-state properties of organic molecules. The performance of each regressor/representation/p
Autor:
Vincent Vanhoucke, George E. Dahl, Tara N. Sainath, Abdelrahman Mohamed, Andrew W. Senior, Geoffrey E. Hinton, Brian Kingsbury, Li Deng, Navdeep Jaitly, Patrick Nguyen, Dong Yu
Publikováno v:
IEEE Signal Processing Magazine. 29:82-97
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coeffi