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

Year Number of Results
1958 1
1962 4
1964 1
1966 1
1968 1
1969 2
1970 3
1971 4
1972 3
1973 5
1974 6
1975 24
1976 27
1977 17
1978 27
1979 20
1980 27
1981 33
1982 23
1983 40
1984 44
1985 51
1986 45
1987 76
1988 86
1989 116
1990 206
1991 270
1992 359
1993 494
1994 559
1995 734
1996 902
1997 895
1998 1022
1999 1103
2000 1180
2001 1328
2002 1328
2003 1571
2004 1903
2005 2096
2006 2223
2007 2447
2008 2707
2009 2929
2010 3003
2011 3418
2012 3599
2013 3932
2014 4289
2015 4871
2016 5234
2017 6245
2018 8522
2019 11523
2020 15147
2021 20313
2022 24643
2023 23565
2024 18008
2025 9

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Search Results

164,002 results

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Page 1
Neural network models and deep learning.
Kriegeskorte N, Golan T. Kriegeskorte N, et al. Curr Biol. 2019 Apr 1;29(7):R231-R236. doi: 10.1016/j.cub.2019.02.034. Curr Biol. 2019. PMID: 30939301 Free article.
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence. ...Finally, we consider how deep neural network models might help us understand brain computation....
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial …
Neural network-based approaches for biomedical relation classification: A review.
Zhang Y, Lin H, Yang Z, Wang J, Sun Y, Xu B, Zhao Z. Zhang Y, et al. J Biomed Inform. 2019 Nov;99:103294. doi: 10.1016/j.jbi.2019.103294. Epub 2019 Sep 23. J Biomed Inform. 2019. PMID: 31557530 Free article. Review.
In this review, we describe the recent advancement of neural network-based approaches for classifying biomedical relations. ...We discuss neural network-based approaches, including convolutional neural networks (CNNs) and recurrent neu …
In this review, we describe the recent advancement of neural network-based approaches for classifying biomedical relations. .. …
A feedforward unitary equivariant neural network.
Ma PW, Chan TH. Ma PW, et al. Neural Netw. 2023 Apr;161:154-164. doi: 10.1016/j.neunet.2023.01.042. Epub 2023 Feb 1. Neural Netw. 2023. PMID: 36745940 Free article.
We devise a new type of feedforward neural network. It is equivariant with respect to the unitary group U(n). ...
We devise a new type of feedforward neural network. It is equivariant with respect to the unitary group U(n). ...
Learning aerodynamics with neural network.
Peng W, Zhang Y, Laurendeau E, Desmarais MC. Peng W, et al. Sci Rep. 2022 Apr 26;12(1):6779. doi: 10.1038/s41598-022-10737-4. Sci Rep. 2022. PMID: 35473951 Free PMC article.
We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. ...We discover that the ESCNN has the ability to extract physical patterns that emerge from aer …
We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards t …
[(2)Neural Network].
Harada T. Harada T. No Shinkei Geka. 2020 Feb;48(2):173-188. doi: 10.11477/mf.1436204155. No Shinkei Geka. 2020. PMID: 32094317 Japanese. No abstract available.
Linking task structure and neural network dynamics.
Márton CD, Zhou S, Rajan K. Márton CD, et al. Nat Neurosci. 2022 Jun;25(6):679-681. doi: 10.1038/s41593-022-01090-w. Nat Neurosci. 2022. PMID: 35668175 Free PMC article.
The solutions neural networks find to solve a task are often inscrutable. We have had little insight into why particular structure emerges in a network. By reverse-engineering neural networks from dynamical principles, Dubreuil & Valente et. …
The solutions neural networks find to solve a task are often inscrutable. We have had little insight into why particular struc …
Domain-adaptive message passing graph neural network.
Shen X, Pan S, Choi KS, Zhou X. Shen X, et al. Neural Netw. 2023 Jul;164:439-454. doi: 10.1016/j.neunet.2023.04.038. Epub 2023 May 3. Neural Netw. 2023. PMID: 37182346
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To address CNNC, we propose a domain-adap …
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the …
Universality of gradient descent neural network training.
Welper G. Welper G. Neural Netw. 2022 Jun;150:259-273. doi: 10.1016/j.neunet.2022.02.016. Epub 2022 Mar 2. Neural Netw. 2022. PMID: 35334438
It has been observed that design choices of neural networks are often crucial for their successful optimization. In this article, we therefore discuss the question if it is always possible to redesign a neural network so that it trains well with gradie …
It has been observed that design choices of neural networks are often crucial for their successful optimization. In this artic …
Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging.
Currie G, Hawk KE, Rohren E, Vial A, Klein R. Currie G, et al. J Med Imaging Radiat Sci. 2019 Dec;50(4):477-487. doi: 10.1016/j.jmir.2019.09.005. Epub 2019 Oct 7. J Med Imaging Radiat Sci. 2019. PMID: 31601480 Review.
An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorith …
An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learni …
164,002 results
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