Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Alex Gittens"'
Publikováno v:
IEEE Access, Vol 10, Pp 120850-120865 (2022)
Model accuracy is the traditional metric employed in machine learning (ML) applications. However, privacy, fairness, and robustness guarantees are crucial as ML algorithms increasingly pervade our lives and play central roles in socially important sy
Externí odkaz:
https://doaj.org/article/cc99fc1eee31462ba87a6bfb9ab06221
Autor:
Alex Gittens, Gabriel Orlanski
Answering a programming question using only its title is difficult as salient contextual information is omitted. Based on this observation, we present a corpus of over 40,000 StackOverflow question texts to be used in conjunction with their correspon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37d37aec6fd9df8e4c7a63f0d8fafa17
http://arxiv.org/abs/2106.04447
http://arxiv.org/abs/2106.04447
Publikováno v:
ICASSP
In recent years, a variety of randomized constructions of sketching matrices have been devised, that have been used in fast algorithms for numerical linear algebra problems, such as least squares regression, low-rank approximation, and the approximat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::160d3ff35d8ba0b82850e5ce1f204053
Publikováno v:
Deployable Machine Learning for Security Defense ISBN: 9783030596200
Malware threat intelligence uncovers deep information about malware, threat actors, and their tactics, Indicators of Compromise, and vulnerabilities in different platforms from scattered threat sources. This collective information can guide decision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab79efbfdc4c88298f16eeb7b783b5e7
https://doi.org/10.1007/978-3-030-59621-7_2
https://doi.org/10.1007/978-3-030-59621-7_2
Publikováno v:
International Journal of Computer Vision. 127:181-206
With significant advances in imaging technology, multiple images of a person or an object are becoming readily available in a number of real-life scenarios. In contrast to single images, image sets can capture a broad range of variations in the appea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db73b47ce677b3189ca29f400d95bbfd
https://doi.org/10.1007/s11263-018-1088-0
https://doi.org/10.1007/s11263-018-1088-0
Publikováno v:
IEEE BigData
Canonical Polyadic Decomposition (CPD) is a powerful technique for uncovering multilinear relationships in tensors. Current research in scalable CPD has focused on designing efficient decomposition algorithms for large sparse tensors that arise in ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07074e58f57e2091640214aeaa2dae82
https://doi.org/10.1109/bigdata.2018.8621879
https://doi.org/10.1109/bigdata.2018.8621879
Autor:
Michael F. Ringenburg, Kristyn Maschhoff, Alex Gittens, Kai Rothauge, Michael W. Mahoney, L. Gerhardt, Shusen Wang, Prabhat, Jey Kottalam
Publikováno v:
Concurrency and Computation: Practice and Experience.
The Apache Spark framework for distributed computation is popular in the data analytics community due to its ease of use, but its MapReduce-style programming model can incur significant overheads when performing computations that do not map directly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fafdee7667a2dd1e0b2dd5b57d6d4ee6
https://doi.org/10.1002/cpe.5026
https://doi.org/10.1002/cpe.5026
Publikováno v:
2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (scalA).
Randomized approaches for low rank matrix approximations have become popular in recent years and often offer significant advantages over classical algorithms because of their scalability and numerical robustness on distributed memory platforms. We pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0542a105f7f74992c999be6ca7aa5ad3
https://doi.org/10.1109/scala.2018.00008
https://doi.org/10.1109/scala.2018.00008
Autor:
Kai Rothauge, Michael W. Mahoney, Jey Kottalam, L. Gerhardt, Michael F. Ringenburg, Alex Gittens, Kristyn Maschhoff, Prabhat, Shusen Wang
Publikováno v:
KDD
Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations that are the basis for solving common machine learning problems---are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3707e0b9de495c56b6008d091d266704
http://arxiv.org/abs/1805.11800
http://arxiv.org/abs/1805.11800
Publikováno v:
ACL (1)
In recent years word-embedding models have gained great popularity due to their remarkable performance on several tasks, including word analogy questions and caption generation. An unexpected “side-effect” of such models is that their vectors oft
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83e04ed801c3093d5707e28c00b18e17
https://doi.org/10.18653/v1/p17-1007
https://doi.org/10.18653/v1/p17-1007