Zobrazeno 1 - 10
of 120
pro vyhledávání: '"John DeNero"'
Publikováno v:
Harvard Data Science Review, Vol 3, Iss 2 (2023)
Externí odkaz:
https://doaj.org/article/b971f3e6dc4b4f2697628d4469cb7f3d
Autor:
Peter R. Carroll, Bin Yu, Anobel Y. Odisho, Briton Park, John DeNero, Nicholas Altieri, Matthew R. Cooperberg
Publikováno v:
JAMIA Open
JAMIA open, vol 3, iss 3
JAMIA open, vol 3, iss 3
Objective Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28b896020be962e560b610e594945603
http://europepmc.org/articles/PMC7751177
http://europepmc.org/articles/PMC7751177
Publikováno v:
Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc0693db3ec13a38993f265a6d62fdd0
https://doi.org/10.1145/3478432.3499154
https://doi.org/10.1145/3478432.3499154
Publikováno v:
JAMIA open, vol 4, iss 3
JAMIA Open
JAMIA Open
ObjectiveWe develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b878ed89e3746bd4f0131f2ec34ba78c
https://escholarship.org/uc/item/4hc8g9v3
https://escholarship.org/uc/item/4hc8g9v3
Publikováno v:
Harvard Data Science Review.
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and the perspective gained by working on real-world problems. We believe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b7a093c9a963bccac73afb6d3573b15
https://doi.org/10.1162/99608f92.cb0fa8d2
https://doi.org/10.1162/99608f92.cb0fa8d2
Autor:
Daniel Fried, David Gaddy, Mitchell Stern, Dan Klein, John DeNero, Nikita Kitaev, Rodolfo Corona
Publikováno v:
Proceedings of the Fifth Workshop on Teaching NLP.
We present a set of assignments for a graduate-level NLP course. Assignments are designed to be interactive, easily gradable, and to give students hands-on experience with several key types of structure (sequences, tags, parse trees, and logical form
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0fd5806c6a196fd2ddec52d586dac2c6
https://doi.org/10.18653/v1/2021.teachingnlp-1.18
https://doi.org/10.18653/v1/2021.teachingnlp-1.18
Publikováno v:
Findings of the Association for Computational Linguistics: EMNLP 2021.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab033d6b8fd3a69e0b849f3466812efa
https://doi.org/10.18653/v1/2021.findings-emnlp.299
https://doi.org/10.18653/v1/2021.findings-emnlp.299
Publikováno v:
ACL
Word alignment was once a core unsupervised learning task in natural language processing because of its essential role in training statistical machine translation (MT) models. Although unnecessary for training neural MT models, word alignment still p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7b9730f32ee54622c36da2d90b9c5f3
http://arxiv.org/abs/2004.14675
http://arxiv.org/abs/2004.14675
Autor:
Nick Parlante, Julie Zelenski, John DeNero, Christopher Allsman, Tiffany Perumpail, Rahul Arya, Kavi Gupta, Catherine Cang, Paul Bitutsky, Ryan Moughan, David J. Malan, Brian Yu, Evan M. Peck, Carl Albing, Kevin Wayne, Keith Schwarz
Publikováno v:
Proceedings of the 51st ACM Technical Symposium on Computer Science Education.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b52b72b4f477f305de5adb8f7fea378d
https://doi.org/10.1145/3328778.3372574
https://doi.org/10.1145/3328778.3372574
Publikováno v:
EMNLP (1)
We propose an efficient batching strategy for variable-length decoding on GPU architectures. During decoding, when candidates terminate or are pruned according to heuristics, our streaming approach periodically "refills" the batch before proceeding w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::443357da5322ec734f0d3d0a3d7c8011