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pro vyhledávání: '"KELLY, BRYAN"'
We open up the black box behind Deep Learning for portfolio optimization and prove that a sufficiently wide and arbitrarily deep neural network (DNN) trained to maximize the Sharpe ratio of the Stochastic Discount Factor (SDF) is equivalent to a larg
Externí odkaz:
http://arxiv.org/abs/2402.06635
The recent discovery of the equivalence between infinitely wide neural networks (NNs) in the lazy training regime and Neural Tangent Kernels (NTKs) (Jacot et al., 2018) has revived interest in kernel methods. However, conventional wisdom suggests ker
Externí odkaz:
http://arxiv.org/abs/2301.11414
Autor:
Kelly, Bryan
ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV
Applications that use TMATS benefit from the abi
Applications that use TMATS benefit from the abi
Externí odkaz:
http://hdl.handle.net/10150/579672
Autor:
Samantha Walczuk, Francesca E. Cunningham, Xinhua Zhao, Diane Dong, Peter A. Glassman, Donald R. Miller, Deborah Khachikian, Anthony Au, Cedric Salone, Kelly Bryan, Qoua Her, Sherrie L. Aspinall
Publikováno v:
Pharmacoepidemiology, Vol 3, Iss 1, Pp 103-116 (2024)
We described insulin glargine (originator) and insulin glargine-yfgn (biosimilar) treatment patterns, assessed effectiveness and safety outcomes, and identified reasons for switching back to the originator product from the biosimilar. This retrospect
Externí odkaz:
https://doaj.org/article/a298ba3cf45d4a1b980727b718f42035
Autor:
Kelly, Bryan
ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada
This paper introduces a set of macros that a
This paper introduces a set of macros that a
Externí odkaz:
http://hdl.handle.net/10150/604487
http://arizona.openrepository.com/arizona/handle/10150/604487
http://arizona.openrepository.com/arizona/handle/10150/604487
Autor:
BYBEE, LELAND (AUTHOR), KELLY, BRYAN (AUTHOR), MANELA, ASAF (AUTHOR) amanela@wustl.edu, XIU, DACHENG (AUTHOR)
Publikováno v:
Journal of Finance (John Wiley & Sons, Inc.). Oct2024, Vol. 79 Issue 5, p3105-3147. 43p.
We introduce a methodology for designing and training deep neural networks (DNN) that we call "Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE ha
Externí odkaz:
http://arxiv.org/abs/2203.05417
Autor:
Uppstrom, Tyler J., Siljander, Breana R., Menta, Samarth V., Baldwin, Robert B., Cecere, Robert, DeFrancesco, Christopher J., Kelly, Bryan T., Ranawat, Amar, Ranawat, Anil S.
Publikováno v:
In The Journal of Arthroplasty September 2024 39(9) Supplement 1:S61-S66
The inclusive potential of synodality: Pope Francis, merciful discernment, and a validity of a kind .
Autor:
Kelly, Bryan P.1 (AUTHOR) kellyb15@tcd.ie
Publikováno v:
Theology & Sexuality: The Journal of the Institute for the Study of Christianity & Sexuality. Jul2024, p1-25. 25p.
Autor:
Rahman, Omar F., Kunze, Kyle N., Yao, Kaisen, Kwiecien, Susan Y., Ranawat, Anil S., Banffy, Michael B., Kelly, Bryan T., Galano, Gregory J.
Publikováno v:
In Arthroscopy: The Journal of Arthroscopic and Related Surgery December 2024 40(12):2840-2849