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Global exponential stability of octonion-valued neural networks with leakage delay and mixed delays.

Neural networks : the official journal of the International Neural Network Society

Popa CA.
PMID: 29890384
Neural Netw. 2018 Sep;105:277-293. doi: 10.1016/j.neunet.2018.05.006. Epub 2018 Jun 14.

This paper discusses octonion-valued neural networks (OVNNs) with leakage delay, time-varying delays, and distributed delays, for which the states, weights, and activation functions belong to the normed division algebra of octonions. The octonion algebra is a nonassociative and noncommutative...

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Popa CA. Global exponential stability of octonion-valued neural networks with leakage delay and mixed delays. Neural Netw. 2018;105:277-293doi: 10.1016/j.neunet.2018.05.006.
Popa, C. A. (2018). Global exponential stability of octonion-valued neural networks with leakage delay and mixed delays. Neural networks : the official journal of the International Neural Network Society, 105277-293. https://doi.org/10.1016/j.neunet.2018.05.006
Popa, Călin-Adrian. "Global exponential stability of octonion-valued neural networks with leakage delay and mixed delays." Neural networks : the official journal of the International Neural Network Society vol. 105 (2018): 277-293. doi: https://doi.org/10.1016/j.neunet.2018.05.006
Popa CA. Global exponential stability of octonion-valued neural networks with leakage delay and mixed delays. Neural Netw. 2018 Sep;105:277-293. doi: 10.1016/j.neunet.2018.05.006. Epub 2018 Jun 14. PMID: 29890384.
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Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

Journal of the American Medical Informatics Association : JAMIA

Xiao C, Choi E, Sun J.
PMID: 29893864
J Am Med Inform Assoc. 2018 Oct 01;25(10):1419-1428. doi: 10.1093/jamia/ocy068.

OBJECTIVE: To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open...

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Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc. 2018;25(10):1419-1428doi: 10.1093/jamia/ocy068.
Xiao, C., Choi, E., & Sun, J. (2018). Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. Journal of the American Medical Informatics Association : JAMIA, 25(10), 1419-1428. https://doi.org/10.1093/jamia/ocy068
Xiao, Cao, et al. "Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review." Journal of the American Medical Informatics Association : JAMIA vol. 25,10 (2018): 1419-1428. doi: https://doi.org/10.1093/jamia/ocy068
Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc. 2018 Oct 01;25(10):1419-1428. doi: 10.1093/jamia/ocy068. PMID: 29893864; PMCID: PMC6188527.
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Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons.

Neural networks : the official journal of the International Neural Network Society

Yang X, Li C, Song Q, Chen J, Huang J.
PMID: 29793129
Neural Netw. 2018 Sep;105:88-103. doi: 10.1016/j.neunet.2018.04.015. Epub 2018 May 04.

This paper talks about the stability and synchronization problems of fractional-order quaternion-valued neural networks (FQVNNs) with linear threshold neurons. On account of the non-commutativity of quaternion multiplication resulting from Hamilton rules, the FQVNN models are separated into four real-valued...

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Yang X, Li C, Song Q, et al. Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons. Neural Netw. 2018;105:88-103doi: 10.1016/j.neunet.2018.04.015.
Yang, X., Li, C., Song, Q., Chen, J., & Huang, J. (2018). Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons. Neural networks : the official journal of the International Neural Network Society, 10588-103. https://doi.org/10.1016/j.neunet.2018.04.015
Yang, Xujun, et al. "Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons." Neural networks : the official journal of the International Neural Network Society vol. 105 (2018): 88-103. doi: https://doi.org/10.1016/j.neunet.2018.04.015
Yang X, Li C, Song Q, Chen J, Huang J. Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons. Neural Netw. 2018 Sep;105:88-103. doi: 10.1016/j.neunet.2018.04.015. Epub 2018 May 04. PMID: 29793129.
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Cell dynamic morphology analysis by deep convolutional features.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Heng Li, Zhiwen Liu, Fengqian Pang, Yonggang Shi.
PMID: 29060456
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:2700-2703. doi: 10.1109/EMBC.2017.8037414.

Computational analysis of cell dynamic morphology in time-lapse image is a challenging task in biomedical research. Inspired by the recent success of deep learning, we investigate the possibility to apply a deep neural network to cell dynamic morphology analysis...

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Heng Li, Zhiwen Liu, Fengqian Pang, et al. Cell dynamic morphology analysis by deep convolutional features. Annu Int Conf IEEE Eng Med Biol Soc. 2017;2017:2700-2703doi: 10.1109/EMBC.2017.8037414.
Heng Li, Zhiwen Liu, Fengqian Pang, & Yonggang Shi. (2017). Cell dynamic morphology analysis by deep convolutional features. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 20172700-2703. https://doi.org/10.1109/EMBC.2017.8037414
Heng Li, et al. "Cell dynamic morphology analysis by deep convolutional features." Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference vol. 2017 (2017): 2700-2703. doi: https://doi.org/10.1109/EMBC.2017.8037414
Heng Li, Zhiwen Liu, Fengqian Pang, Yonggang Shi. Cell dynamic morphology analysis by deep convolutional features. Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:2700-2703. doi: 10.1109/EMBC.2017.8037414. PMID: 29060456.
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Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks.

Studies in health technology and informatics

Tang G, Ni Y, Wang K, Yong Q.
PMID: 29678055
Stud Health Technol Inform. 2018;247:720-724.

The online patient question and answering (Q&A) system attracts an increasing amount of users in China. Patient will post their questions and wait for doctors' response. To avoid the lag time involved with the waiting and to reduce the...

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Tang G, Ni Y, Wang K, et al. Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks. Stud Health Technol Inform. 2018;247:720-724
Tang, G., Ni, Y., Wang, K., & Yong, Q. (2018). Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks. Studies in health technology and informatics, 247720-724.
Tang, Guoyu, et al. "Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks." Studies in health technology and informatics vol. 247 (2018): 720-724.
Tang G, Ni Y, Wang K, Yong Q. Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks. Stud Health Technol Inform. 2018;247:720-724. PMID: 29678055.
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Efficient construction of sparse radial basis function neural networks using L.

Neural networks : the official journal of the International Neural Network Society

Qian X, Huang H, Chen X, Huang T.
PMID: 28806717
Neural Netw. 2017 Oct;94:239-254. doi: 10.1016/j.neunet.2017.07.004. Epub 2017 Jul 27.

This paper investigates the construction of sparse radial basis function neural networks (RBFNNs) for classification problems. An efficient two-phase construction algorithm (which is abbreviated as TPCLR

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Qian X, Huang H, Chen X, et al. Efficient construction of sparse radial basis function neural networks using L. Neural Netw. 2017;94:239-254doi: 10.1016/j.neunet.2017.07.004.
Qian, X., Huang, H., Chen, X., & Huang, T. (2017). Efficient construction of sparse radial basis function neural networks using L. Neural networks : the official journal of the International Neural Network Society, 94239-254. https://doi.org/10.1016/j.neunet.2017.07.004
Qian, Xusheng, et al. "Efficient construction of sparse radial basis function neural networks using L." Neural networks : the official journal of the International Neural Network Society vol. 94 (2017): 239-254. doi: https://doi.org/10.1016/j.neunet.2017.07.004
Qian X, Huang H, Chen X, Huang T. Efficient construction of sparse radial basis function neural networks using L. Neural Netw. 2017 Oct;94:239-254. doi: 10.1016/j.neunet.2017.07.004. Epub 2017 Jul 27. PMID: 28806717.
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Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints.

Medical image analysis

Borne L, Rivière D, Mancip M, Mangin JF.
PMID: 32163879
Med Image Anal. 2020 May;62:101651. doi: 10.1016/j.media.2020.101651. Epub 2020 Feb 28.

The extreme variability of the folding pattern of the human cortex makes the recognition of cortical sulci, both automatic and manual, particularly challenging. Reliable identification of the human cortical sulci in its entirety, is extremely difficult and is practiced...

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Borne L, Rivière D, Mancip M, et al. Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints. Med Image Anal. 2020;62:101651doi: 10.1016/j.media.2020.101651.
Borne, L., Rivière, D., Mancip, M., & Mangin, J. F. (2020). Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints. Medical image analysis, 62101651. https://doi.org/10.1016/j.media.2020.101651
Borne, Léonie, et al. "Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints." Medical image analysis vol. 62 (2020): 101651. doi: https://doi.org/10.1016/j.media.2020.101651
Borne L, Rivière D, Mancip M, Mangin JF. Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints. Med Image Anal. 2020 May;62:101651. doi: 10.1016/j.media.2020.101651. Epub 2020 Feb 28. PMID: 32163879.
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Reply to the letter to the editor 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al.

Annals of oncology : official journal of the European Society for Medical Oncology

Haenssle HA, Fink C, Rosenberger A, Uhlmann L.
PMID: 30689691
Ann Oncol. 2019 May 01;30(5):854-857. doi: 10.1093/annonc/mdz015.

No abstract available.

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Haenssle HA, Fink C, Rosenberger A, et al. Reply to the letter to the editor 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al. Ann Oncol. 2019;30(5):854-857doi: 10.1093/annonc/mdz015.
Haenssle, H. A., Fink, C., Rosenberger, A., & Uhlmann, L. (2019). Reply to the letter to the editor 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al. Annals of oncology : official journal of the European Society for Medical Oncology, 30(5), 854-857. https://doi.org/10.1093/annonc/mdz015
Haenssle, H A, et al. "Reply to the letter to the editor 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al." Annals of oncology : official journal of the European Society for Medical Oncology vol. 30,5 (2019): 854-857. doi: https://doi.org/10.1093/annonc/mdz015
Haenssle HA, Fink C, Rosenberger A, Uhlmann L. Reply to the letter to the editor 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by H. A. Haenssle et al. Ann Oncol. 2019 May 01;30(5):854-857. doi: 10.1093/annonc/mdz015. PMID: 30689691.
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Fast generalization error bound of deep learning without scale invariance of activation functions.

Neural networks : the official journal of the International Neural Network Society

Terada Y, Hirose R.
PMID: 32593931
Neural Netw. 2020 Sep;129:344-358. doi: 10.1016/j.neunet.2020.05.033. Epub 2020 Jun 22.

In the theoretical analysis of deep learning, discovering which features of deep learning lead to good performance is an important task. Using the framework for analyzing the generalization error developed by Suzuki (2018), we derive a fast learning rate...

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Terada Y, Hirose R. Fast generalization error bound of deep learning without scale invariance of activation functions. Neural Netw. 2020;129:344-358doi: 10.1016/j.neunet.2020.05.033.
Terada, Y., & Hirose, R. (2020). Fast generalization error bound of deep learning without scale invariance of activation functions. Neural networks : the official journal of the International Neural Network Society, 129344-358. https://doi.org/10.1016/j.neunet.2020.05.033
Terada, Yoshikazu, and Hirose, Ryoma. "Fast generalization error bound of deep learning without scale invariance of activation functions." Neural networks : the official journal of the International Neural Network Society vol. 129 (2020): 344-358. doi: https://doi.org/10.1016/j.neunet.2020.05.033
Terada Y, Hirose R. Fast generalization error bound of deep learning without scale invariance of activation functions. Neural Netw. 2020 Sep;129:344-358. doi: 10.1016/j.neunet.2020.05.033. Epub 2020 Jun 22. PMID: 32593931.
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Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout.

Journal of chemical information and modeling

Cortés-Ciriano I, Bender A.
PMID: 31241929
J Chem Inf Model. 2019 Jul 22;59(7):3330-3339. doi: 10.1021/acs.jcim.9b00297. Epub 2019 Jun 26.

While the use of deep learning in drug discovery is gaining increasing attention, the lack of methods to compute reliable errors in prediction for Neural Networks prevents their application to guide decision making in domains where identifying unreliable predictions...

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Cortés-Ciriano I, Bender A. Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout. J Chem Inf Model. 2019;59(7):3330-3339doi: 10.1021/acs.jcim.9b00297.
Cortés-Ciriano, I., & Bender, A. (2019). Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout. Journal of chemical information and modeling, 59(7), 3330-3339. https://doi.org/10.1021/acs.jcim.9b00297
Cortés-Ciriano, Isidro, and Bender, Andreas. "Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout." Journal of chemical information and modeling vol. 59,7 (2019): 3330-3339. doi: https://doi.org/10.1021/acs.jcim.9b00297
Cortés-Ciriano I, Bender A. Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout. J Chem Inf Model. 2019 Jul 22;59(7):3330-3339. doi: 10.1021/acs.jcim.9b00297. Epub 2019 Jun 26. PMID: 31241929.
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Deep learning methods and applications in neuroimaging.

Journal of neuroscience methods

Sui J, Liu M, Lee JH, Zhang J, Calhoun V.
PMID: 32272117
J Neurosci Methods. 2020 Jun 01;339:108718. doi: 10.1016/j.jneumeth.2020.108718. Epub 2020 Apr 06.

No abstract available.

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Sui J, Liu M, Lee JH, et al. Deep learning methods and applications in neuroimaging. J Neurosci Methods. 2020;339:108718doi: 10.1016/j.jneumeth.2020.108718.
Sui, J., Liu, M., Lee, J. H., Zhang, J., & Calhoun, V. (2020). Deep learning methods and applications in neuroimaging. Journal of neuroscience methods, 339108718. https://doi.org/10.1016/j.jneumeth.2020.108718
Sui, Jing, et al. "Deep learning methods and applications in neuroimaging." Journal of neuroscience methods vol. 339 (2020): 108718. doi: https://doi.org/10.1016/j.jneumeth.2020.108718
Sui J, Liu M, Lee JH, Zhang J, Calhoun V. Deep learning methods and applications in neuroimaging. J Neurosci Methods. 2020 Jun 01;339:108718. doi: 10.1016/j.jneumeth.2020.108718. Epub 2020 Apr 06. PMID: 32272117.
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Long data from the electrocardiogram.

Lancet (London, England)

Quer G, Muse ED, Topol EJ, Steinhubl SR.
PMID: 31162070
Lancet. 2019 Jun 01;393(10187):2189. doi: 10.1016/S0140-6736(19)31186-9.

No abstract available.

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Quer G, Muse ED, Topol EJ, et al. Long data from the electrocardiogram. Lancet. 2019;393(10187):2189doi: 10.1016/S0140-6736(19)31186-9.
Quer, G., Muse, E. D., Topol, E. J., & Steinhubl, S. R. (2019). Long data from the electrocardiogram. Lancet (London, England), 393(10187), 2189. https://doi.org/10.1016/S0140-6736(19)31186-9
Quer, Giorgio, et al. "Long data from the electrocardiogram." Lancet (London, England) vol. 393,10187 (2019): 2189. doi: https://doi.org/10.1016/S0140-6736(19)31186-9
Quer G, Muse ED, Topol EJ, Steinhubl SR. Long data from the electrocardiogram. Lancet. 2019 Jun 01;393(10187):2189. doi: 10.1016/S0140-6736(19)31186-9. PMID: 31162070.
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Showing 1 to 12 of 328 entries