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pro vyhledávání: '"Northcutt, Curtis G"'
Autor:
Oala, Luis, Maskey, Manil, Bat-Leah, Lilith, Parrish, Alicia, Gürel, Nezihe Merve, Kuo, Tzu-Sheng, Liu, Yang, Dror, Rotem, Brajovic, Danilo, Yao, Xiaozhe, Bartolo, Max, Rojas, William A Gaviria, Hileman, Ryan, Aliment, Rainier, Mahoney, Michael W., Risdal, Meg, Lease, Matthew, Samek, Wojciech, Dutta, Debojyoti, Northcutt, Curtis G, Coleman, Cody, Hancock, Braden, Koch, Bernard, Tadesse, Girmaw Abebe, Karlaš, Bojan, Alaa, Ahmed, Dieng, Adji Bousso, Noy, Natasha, Reddi, Vijay Janapa, Zou, James, Paritosh, Praveen, van der Schaar, Mihaela, Bollacker, Kurt, Aroyo, Lora, Zhang, Ce, Vanschoren, Joaquin, Guyon, Isabelle, Mattson, Peter
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will adva
Externí odkaz:
http://arxiv.org/abs/2311.13028
Publikováno v:
35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks
We identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results. Errors in test sets are numerous
Externí odkaz:
http://arxiv.org/abs/2103.14749
The ability to combine symbols to generate language is a defining characteristic of human intelligence, particularly in the context of artistic story-telling through lyrics. We develop a method for synthesizing a rap verse based on the content of any
Externí odkaz:
http://arxiv.org/abs/2004.03965
Publikováno v:
Proceedings of the Sixth {ACM} Conference on Learning @ Scale, L@S 2017
Viewing consumption of discussion forums with hundreds or more comments depends on ranking because most users only view top-ranked comments. When comments are ranked by an ordered score (e.g. number of replies or up-votes) without adjusting for seman
Externí odkaz:
http://arxiv.org/abs/2002.12457
Publikováno v:
Journal of Artificial Intelligence Research (JAIR) (2021)
Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying lab
Externí odkaz:
http://arxiv.org/abs/1911.00068
Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose Rank Pruning (RP) to solve noisy PN learning
Externí odkaz:
http://arxiv.org/abs/1705.01936
We describe a cheating strategy enabled by the features of massive open online courses (MOOCs) and detectable by virtue of the sophisticated data systems that MOOCs provide. The strategy, Copying Answers using Multiple Existences Online (CAMEO), invo
Externí odkaz:
http://arxiv.org/abs/1508.05699
Akademický článek
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Publikováno v:
In Computers & Education September 2016 100:71-80
Akademický článek
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