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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1949 1
1950 1
1951 1
1952 1
1953 3
1954 1
1956 1
1957 1
1958 1
1960 2
1961 3
1962 6
1963 5
1964 4
1965 2
1968 2
1969 2
1971 1
1972 1
1974 2
1976 2
1978 3
1979 2
1980 1
1983 4
1984 3
1985 33
1986 127
1987 248
1988 348
1989 449
1990 502
1991 556
1992 619
1993 839
1994 954
1995 990
1996 972
1997 1148
1998 1209
1999 1260
2000 1534
2001 1686
2002 2068
2003 3109
2004 4841
2005 5638
2006 6382
2007 7037
2008 7833
2009 7350
2010 7633
2011 8145
2012 9254
2013 9914
2014 9355
2015 8380
2016 7485
2017 7808
2018 8722
2019 4371
2020 30
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124,679 results
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Page 1
Deep Learning in Medical Imaging: General Overview
Lee JG, et al. Korean J Radiol 2017 - Review. PMID 28670152 Free PMC article.
Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. ...
Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. ...
Many regression algorithms, one unified model: A review.
Stulp F and Sigaud O. Neural Netw 2015 - Review. PMID 26087306
Our ambition is thus to provide a deep understanding of the relationship between these algorithms, that, despite being derived from very different principles, use a function representation that can be captured within one unified model. Finally, step-by-step derivations of the algorithms from first principles and visualizations of their inner workings allow this article to be used as a tutorial for those new to regression....
Our ambition is thus to provide a deep understanding of the relationship between these algorithms, that, despite being derived from very different principles, use a function representation that can be captured within one unified model. Finally, step-by-step derivations of the algorithms
Deep learning algorithm
Sears C, et al. Surv Ophthalmol 2018. PMID 29248535
On computational algorithms for real-valued continuous functions of several variables.
Sprecher D. Neural Netw 2014. PMID 25036646
The subject of this paper is algorithms for computing superpositions of real-valued continuous functions of several variables based on space-filling curves. ...After discussing the link between the algorithms and these curves, this paper presents two algorithms for the case of two variables: one based on space-filling curves with worked out coding, and the Hilbert curve (Hilbert, 1891)....
The subject of this paper is algorithms for computing superpositions of real-valued continuous functions of several variables based on space-filling curves. ...After discussing the link between the algorithms and these curves, this paper presents two algorithms for the case of t …
Multilayer bootstrap networks
Zhang XL. Neural Netw 2018. PMID 29625354 Free article.
Hints
Abu-Mostafa YS. Neural Comput 1995 - Review. PMID 7584883
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