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Topology and design of ANN

1. Weibo Liu and others, A survey of deep neural network architectures and their applications, Neurocomputing Volume 234, 19 April 2017, Pages 11-26

2. J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks,  Volume 61, January 2015, Pages 85-117

3. Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press book, 2016

4. Collection of  papers:  http://deeplearning.net/reading-list/

5. Understanding LSTM  & Recurrent Neural Networks  Networks, article web

6. Convolutional Neural Networks,  article web,    article web

7.  Strack, Rita. "Deep Learning Advances Super-resolution Imaging." Nature methods 15, no. 6 (2018)

8. Big data needs a hardware revolution,  Nature 554, 145-146 (2018)


ANN Training

1. D. Stathakis, How many hidden layers and nodes?,  Journal International Journal of Remote Sensing
Volume 30, 2009 - Issue 8

2. Montana, D. J., & Davis, L. (1989). Training feedforward neural networks using genetic algorithms. In Proceedings of the 11th international joint conference on
artificial intelligence—vol. 1 (pp. 762–767). San Francisco

3. Leslie N. Smith, A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay, arxiv.org/abs/1803.09820

Applications to game theory

David Silver and others, Mastering the game of Go without human knowledge, Nature | vol 550, 19 october 2017

ANN and Human Behaviour

1. Z. Zhang, F. Vanderhaegen, and P. Millot, Uncertainty analysis in the prediction of human operator violation using neural networks, WSEAS Transactions on Circuits and systems, vol.3, issue.2, pp.259-265, 2004.

2. J.N. Towse, D. Neil Analyzing human random generation behavior: A review of methods used and a computer program for describing performance
Behavior Research Methods, Instruments, & Computers, 30 (1998), pp. 583-591

3. Wagenaar, W. A. (1972) Generation of random sequences by human subjects: A critical survey of literature. Psychological Bulletin 77:65–72.

4. R. Schulz and others, "Lingodroids: Studies in spatial cognition and language," 2011 IEEE International Conference on Robotics and Automation, Shanghai, 2011, pp. 178-183.

ANN and Biology & Genetics

1. Webb, Sarah. "Deep Learning for Biology" Nature 554, no. 7693 (2018)

2. Liberis, Edgar, "Parapred: Antibody Paratope Prediction Using Convolutional and Recurrent Neural Networks." Bioinformatics (Oxford, England)

3. Franklin, Nicholas T, and Michael J Frank. "Compositional Clustering in Task Structure Learning" PLoS computational biology 14, no. 4 (2018)

4. Maramis and others, "IRProfiler - a Software Toolbox for High Throughput Immune Receptor Profiling." BMC bioinformatics 19, no. 1 (2018)

5. Adolf-Bryfogle and others "RosettaAntibodyDesign (RAbD): A General Framework for Computational Antibody Design." PLoS computational biology 14, no. 4 (2018)

6. Zeng and others  "ComplexContact: A Web Server for Inter-protein Contact Prediction Using Deep Learning." Nucleic acids research (2018)

7. Quang and others  "YAMDA: Thousandfold Speedup of EM-based Motif Discovery Using Deep Learning Libraries and GPU." Bioinformatics (Oxford, England) (2018)

8. Hutson, Matthew. "Boycott Highlights AI's Publishing Rebellion." Science (New York, N.Y.) 360, no. 6390 (2018)

9. How artificial intelligence is changing drug discovery, Nature 557, S55-S57 (2018)

10. Gligorijevic, Vladimir, Meet Barot, and Richard Bonneau. "DeepNF: Deep Network Fusion for Protein Function Prediction." Bioinformatics (Oxford, England) (2018)

11.  Yang and others  "LncADeep: An Ab Initio LncRNA Identification and Functional Annotation Tool Based on Deep Learning." Bioinformatics (Oxford, England) (2018)

12.  Baek and others  "LncRNAnet: Long Non-coding RNA Identification Using Deep Learning." Bioinformatics (Oxford, England) (2018)

13.  Wen and others, "DeepMirTar: A Deep-learning Approach for Predicting Human MiRNA Targets." Bioinformatics (Oxford, England) (2018)

14. Li, Yifeng and others "Genome-wide Prediction of Cis-regulatory Regions Using Supervised Deep Learning Methods." BMC bioinformatics 19, no. 1 (2018)