127 related articles for article (PubMed ID: 30978610)
1. Equivalence between dropout and data augmentation: A mathematical check.
Zhao D; Yu G; Xu P; Luo M
Neural Netw; 2019 Jul; 115():82-89. PubMed ID: 30978610
[TBL] [Abstract][Full Text] [Related]
2. Regularization of deep neural networks with spectral dropout.
Khan SH; Hayat M; Porikli F
Neural Netw; 2019 Feb; 110():82-90. PubMed ID: 30504041
[TBL] [Abstract][Full Text] [Related]
3. Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.
Steven Eyobu O; Han DS
Sensors (Basel); 2018 Aug; 18(9):. PubMed ID: 30200377
[TBL] [Abstract][Full Text] [Related]
4. Towards dropout training for convolutional neural networks.
Wu H; Gu X
Neural Netw; 2015 Nov; 71():1-10. PubMed ID: 26277608
[TBL] [Abstract][Full Text] [Related]
5. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.
Pang S; Yu Z; Orgun MA
Comput Methods Programs Biomed; 2017 Mar; 140():283-293. PubMed ID: 28254085
[TBL] [Abstract][Full Text] [Related]
6. Forward propagation dropout in deep neural networks using Jensen-Shannon and random forest feature importance ranking.
Heidari M; Moattar MH; Ghaffari H
Neural Netw; 2023 Aug; 165():238-247. PubMed ID: 37307667
[TBL] [Abstract][Full Text] [Related]
7. Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines.
Shen M; Yang J; Li S; Zhang A; Bai Q
Micromachines (Basel); 2021 Nov; 12(12):. PubMed ID: 34945353
[TBL] [Abstract][Full Text] [Related]
8. Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.
Poernomo A; Kang DK
Neural Netw; 2018 Aug; 104():60-67. PubMed ID: 29715684
[TBL] [Abstract][Full Text] [Related]
9. Why ResNet Works? Residuals Generalize.
He F; Liu T; Tao D
IEEE Trans Neural Netw Learn Syst; 2020 Dec; 31(12):5349-5362. PubMed ID: 32031953
[TBL] [Abstract][Full Text] [Related]
10. On joint parameterizations of linear and nonlinear functionals in neural networks.
Atto AM; Galichet S; Pastor D; Méger N
Neural Netw; 2023 Mar; 160():12-21. PubMed ID: 36592526
[TBL] [Abstract][Full Text] [Related]
11. Deep neural networks for texture classification-A theoretical analysis.
Basu S; Mukhopadhyay S; Karki M; DiBiano R; Ganguly S; Nemani R; Gayaka S
Neural Netw; 2018 Jan; 97():173-182. PubMed ID: 29126070
[TBL] [Abstract][Full Text] [Related]
12. Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.
Kooi T; van Ginneken B; Karssemeijer N; den Heeten A
Med Phys; 2017 Mar; 44(3):1017-1027. PubMed ID: 28094850
[TBL] [Abstract][Full Text] [Related]
13. Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.
Dürr O; Murina E; Siegismund D; Tolkachev V; Steigele S; Sick B
Assay Drug Dev Technol; 2018; 16(6):343-349. PubMed ID: 30148665
[TBL] [Abstract][Full Text] [Related]
14. tRNA-DL: A Deep Learning Approach to Improve tRNAscan-SE Prediction Results.
Gao X; Wei Z; Hakonarson H
Hum Hered; 2018; 83(3):163-172. PubMed ID: 30685762
[TBL] [Abstract][Full Text] [Related]
15. Linear constraints on weight representation for generalized learning of multilayer networks.
Ishii M; Kumazawa I
Neural Comput; 2001 Dec; 13(12):2851-63. PubMed ID: 11705413
[TBL] [Abstract][Full Text] [Related]
16. Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.
Li H; Yang M; Chen Q; Tang B; Wang X; Yan J
BMC Med Inform Decis Mak; 2018 Jul; 18(Suppl 2):60. PubMed ID: 30066652
[TBL] [Abstract][Full Text] [Related]
17. Robustness of Sparsely Distributed Representations to Adversarial Attacks in Deep Neural Networks.
Sardar N; Khan S; Hintze A; Mehra P
Entropy (Basel); 2023 Jun; 25(6):. PubMed ID: 37372277
[TBL] [Abstract][Full Text] [Related]
18. Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval.
ElAdel A; Zaied M; Amar CB
Neural Netw; 2017 Nov; 95():10-18. PubMed ID: 28843091
[TBL] [Abstract][Full Text] [Related]
19. Information Dropout: Learning Optimal Representations Through Noisy Computation.
Achille A; Soatto S
IEEE Trans Pattern Anal Mach Intell; 2018 Dec; 40(12):2897-2905. PubMed ID: 29994167
[TBL] [Abstract][Full Text] [Related]
20. The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining.
Świć A; Wołos D; Gola A; Kłosowski G
Sensors (Basel); 2020 Aug; 20(17):. PubMed ID: 32825114
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
