and design of ANN
1. Weibo Liu and others, A survey of deep neural network
architectures and their applications
Volume 234, 19 April 2017, Pages 11-26
2. J. Schmidhuber, Deep learning in neural networks: An
, Neural Networks, Volume 61, January 2015,
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
6. Convolutional Neural Networks, article
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)
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.
Proceedings of the 11th international joint conference on
artificial intelligence—vol. 1 (pp. 762–767). San Francisco
Leslie N. Smith, A disciplined approach to neural network
hyper-parameters: Part 1 -- learning rate, batch size,
momentum, and weight decay
to game theory
David Silver and others, Mastering the game of Go without
Nature | vol 550, 19 october 2017
and Human Behaviour
1. Z. Zhang, F. Vanderhaegen, and P. Millot, Uncertainty
analysis in the prediction of human operator violation using
, 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
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.
and Biology & Genetics
1. Webb, Sarah. "Deep Learning for Biology"
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"
biology 14, no. 4 (2018)
4. Maramis and others, "IRProfiler - a Software Toolbox for
High Throughput Immune Receptor Profiling."
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)
Quang and others "YAMDA: Thousandfold Speedup of
EM-based Motif Discovery Using Deep Learning Libraries and
Bioinformatics (Oxford, England) (2018)
Hutson, Matthew. "Boycott Highlights AI's Publishing
Science (New York, N.Y.) 360, no. 6390 (2018)