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pro vyhledávání: '"J. Gaynier"'
Background: The literature suggests that medical student self-assessment often does not match the evaluations from faculty/residents but can improve over time and with practice. Additionally, there is literature that suggests women underestimate thei
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7763b110c68319797880fad6708050c
https://doi.org/10.21203/rs.3.rs-544230/v1
https://doi.org/10.21203/rs.3.rs-544230/v1
Autor:
David Gelovani, Sahrish Ilyas, Madeline Berschback, Henry Y. Kwon, Asra N. Shaik, Diane Levine, Anthony J. Gaynier, Lea M. Monday
Publikováno v:
Cureus
Introduction Changes in medical education and health care delivery have limited the ability of fourth-year medical students to perform the role of an intern prior to graduating from medical school. To address this issue, many schools have instituted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b587bfdfd739a25d6a2083dcc55d9e06
https://pubmed.ncbi.nlm.nih.gov/32670695
https://pubmed.ncbi.nlm.nih.gov/32670695
Publikováno v:
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 815:7-17
An Allison-type emittance scanner has been designed for PXIE at FNAL with the goal of providing fast and accurate phase space reconstruction. The device has been modified from previous LBNL/SNS designs to operate in both pulsed and DC modes with the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e56f602adbd082d149b72e7a24aaeba
https://doi.org/10.1016/j.nima.2016.01.039
https://doi.org/10.1016/j.nima.2016.01.039
Autor:
R. J. Gaynier, Tom Downs
Publikováno v:
Mathematical and Computer Modelling. 22:53-61
Artificial neural networks have, in recent years, been very successfully applied in a wide range of areas. A major reason for this success has been the existence of a training algorithm called backpropagation. This algorithm relies upon the neural un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7e9638184fb50119ea231c7a2b14419
https://doi.org/10.1016/0895-7177(95)00180-a
https://doi.org/10.1016/0895-7177(95)00180-a
Autor:
Tom Downs, R. J. Gaynier
Publikováno v:
Neural Networks. 8:901-904
It is sometimes stated that neural networks that employ units with nonmonotonic transfer functions are more difficult to train than networks that use monotonic transfer functions, because the former can be expected to have more local minima. That thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5719b7b8b8ecb5d1a45094889e4f1558
https://doi.org/10.1016/0893-6080(95)00040-7
https://doi.org/10.1016/0893-6080(95)00040-7
Autor:
Tom Downs, R. J. Gaynier
Publikováno v:
ICNN
A learning algorithm is presented that uses internal representations, which are continuous random variables, for the training of multilayer networks whose neurons have Heaviside characteristics. This algorithm is an improvement in that it is applicab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2e5b656dbfd35c9ddcb03c0454637f9
https://doi.org/10.1109/icnn.1993.298832
https://doi.org/10.1109/icnn.1993.298832
Autor:
P. K. Purkayastha, Larry J. Gaynier
Publikováno v:
SAE Technical Paper Series.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d3ece9f1bfa3add412d449fc694b896
https://doi.org/10.4271/1999-01-0951
https://doi.org/10.4271/1999-01-0951
Autor:
Tom Downs, R. J. Gaynier
Publikováno v:
ICANN ’94 ISBN: 9783540198871
In the last few years several alternatives to the mean-squared error function have been proposed as a means of improving the speed of backpropagation learning. Perhaps best-known among these are the functions considered in (Fahlman 1988) and the cros
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd28804842eeb8fd60af1b867cea3f23
https://doi.org/10.1007/978-1-4471-2097-1_130
https://doi.org/10.1007/978-1-4471-2097-1_130
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