Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Aaron Klein"'
Publikováno v:
RSF: The Russell Sage Foundation Journal of the Social Sciences, Vol 3, Iss 1, Pp 20-47 (2017)
This article assesses the benefits and costs of key provisions of the Dodd-Frank Act that strengthened regulation following the financial crisis. The provisions are placed into five groupings: clear wins, clear losses, costly tradeoffs, unfinished bu
Externí odkaz:
https://doaj.org/article/5cccf124b0634cfea1ab28119d63535f
Autor:
Aaron Klein
Publikováno v:
Business Economics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a2d1ef50ca21f785b4a83e43ee73e3c
https://doi.org/10.1057/s11369-023-00321-7
https://doi.org/10.1057/s11369-023-00321-7
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa7f9ecd114bae516be39e72b8ff49a2
https://doi.org/10.2139/ssrn.4049321
https://doi.org/10.2139/ssrn.4049321
Autor:
Taylor Wilcox, Aaron Kleinertz, Benjamin D Seadler, Lyle D Joyce, John Charlson, Paul L Linsky
Publikováno v:
Rare Tumors, Vol 16 (2024)
Soft tissue sarcomas account for less than 1% of new cancer diagnoses, approximately one in five of which are liposarcomas. These tumors typically arise in the deep tissues of the proximal extremity or retroperitoneum, with just under 3% presenting a
Externí odkaz:
https://doaj.org/article/082aa604af7747868847db37918085de
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 395:114991
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f99ab42bd5dc8361f6c8d8a7bb735ef
https://doi.org/10.1016/j.cma.2022.114991
https://doi.org/10.1016/j.cma.2022.114991
Autor:
Patrick B Murphy, Joshua Dilday, Aaron Kleinertz, Kenrick Manswell, Kent Peterson, Colleen Flanagan, Melissa Drezdzon
Publikováno v:
Trauma Surgery & Acute Care Open, Vol 9, Iss 1 (2024)
Externí odkaz:
https://doaj.org/article/429eceae4719418a87f351b6e5358691
Autor:
Michael Burkart, Hector Mendoza, Jost Tobias Springenberg, Maximilian Dippel, Matthias Urban, Aaron Klein, Frank Hutter, Marius Lindauer, Matthias Feurer
Publikováno v:
Automated Machine Learning ISBN: 9783030053178
Automated Machine Learning
Automated Machine Learning
Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. In this work, we present two versions of Auto-Net, which provide automatically-tuned deep neural networks without any h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bc69bfff9d8fc888aa05baf11b5bd82
https://doi.org/10.1007/978-3-030-05318-5_7
https://doi.org/10.1007/978-3-030-05318-5_7
Autor:
Frank Hutter, Manuel Blum, Katharina Eggensperger, Jost Tobias Springenberg, Aaron Klein, Matthias Feurer
Publikováno v:
Automated Machine Learning ISBN: 9783030053178
Automated Machine Learning
Automated Machine Learning
The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a go
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa637491e05235736e45880cfe4befd1
https://doi.org/10.1007/978-3-030-05318-5_6
https://doi.org/10.1007/978-3-030-05318-5_6
Publikováno v:
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computer Vision – ECCV 2018
Computer Vision – ECCV 2018-15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII
Computer Vision – ECCV 2018 ISBN: 9783030012335
ECCV (7)
Lecture Notes in Computer Science-Computer Vision – ECCV 2018
Computer Vision – ECCV 2018-15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII
Computer Vision – ECCV 2018 ISBN: 9783030012335
ECCV (7)
Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology. In this paper, we make such networks estimate their local un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b622770c9802bee0b574d8dddaf5279
We present a toolchain for computational research consisting of Sacred and two supporting tools. Sacred is an open source Python framework which aims to provide basic infrastructure for running computational experimentsindependent of the methods and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5378d934f2d23e214b822e653bedf8b9