LeNet（四）：Appendix

 LeNet（四）：Appendix

2021/04/10

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以下只列出論文

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# Guide BP

Springenberg, Jost Tobias, et al. "Striving for simplicity: The all convolutional net." arXiv preprint arXiv:1412.6806 (2014).

https://arxiv.org/pdf/1412.6806.pdf

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# 01 ML F7 F1

Alom, Md Zahangir, et al. "The history began from alexnet: A comprehensive survey on deep learning approaches." arXiv preprint arXiv:1803.01164 (2018).

https://arxiv.org/ftp/arxiv/papers/1803/1803.01164.pdf

# 02 DL F1 F2 F5

LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.

https://www2.cs.duke.edu/courses/spring19/compsci527/papers/Lecun.pdf

# 03 CV F1

Gu, Jiuxiang, et al. "Recent advances in convolutional neural networks." Pattern Recognition 77 (2018): 354-377.

https://arxiv.org/pdf/1512.07108.pdf

04 NLP WE F2 recent trends in deep learning based natural language processing

05 NLP SA F14 a survey of deep learning techniques for natural machine translation

06 Net F3 a survry of the recent architectures of deep convolutional neural networks

07 Franework T1 T2 a review of deep learning with special emphasis on architectures, applications and recent trends

08 Vector F3 F4 towards bayesian deep learning: a survey

09 BP P12 deep learnig in neural networks: an overview

10 Regularization F11 deep convolutional neural networks for image classification: a comprehensive review

11 TL F1 representation learning: a review and new perspectives

12 SS F41 image segmentation using deep learning: a survey

13 OD F4 deep learning for generic object detection: a aurvey

14 OD F2 object detection in 20 years: a survey

15 OD F1 a survey of deep learning-based object detection

16 OD F1 imbalance problem in object detection: a review

17 NAS T1 neural architecture search: a survey

18 GAN F1 generative adversarial networks: an overview

19 GAN F3 recent progress on generative adversarial networks(GANS): a survey

20 GAN T1 an introduction to image synthesis with generative adversarial nets

21 RL F2 F3 F4 deep reinforcement learning a brief survey

22 RL F2 F5 system design perspective for human-level agents using deep reinforcement learning: a survey

23 RL 8 As deep reinforcement learning: an overview

24 RL 18 As algorithms for reinforcement learning

25 G F1 T1 T9 deep learning on graphs: a survey

26 GNN graph neural networks: a review of methods and applications

27 GNN a comprehensive survey on graph neural networks

28 GE graph embedding techniques, applications, and performance: a survey

29 GE a comprehensive survey of graph embedding: problems, techniques and applications

30 Speech speech recognition using deep neural networks: a systemic review

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