References

Neural Network 101

LeCun, Yann A., et al. "Efficient backprop." Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 9–48.

Universal Approximability

Cybenko, George. "Approximation by superpositions of a sigmoidal function." Mathematics of Control, Signals, and Systems (MCSS) 2.4 (1989): 303-314.
Hornik, Kurt. "Approximation capabilities of multilayer feedforward networks." Neural networks 4.2 (1991): 251-257.
Sonoda, Sho, and Noboru Murata. "Neural network with unbounded activation functions is universal approximator." Applied and Computational Harmonic Analysis (2015).

Activation Function

Bengio, Yoshua, Patrice Simard, and Paolo Frasconi. "Learning long-term dependencies with gradient descent is difficult." IEEE transactions on neural networks 5.2 (1994): 157-166.
Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. "Deep Sparse Rectifier Neural Networks." International Conference on Artificial Intelligence and Statistics. 2011.
Goodfellow, Ian, et al. "Maxout Networks." Proceedings of the 30th International Conference on Machine Learning (ICML-13). 2013.
Springenberg, Jost Tobias, and Martin Riedmiller. "Improving deep neural networks with probabilistic maxout units." arXiv preprint arXiv:1312.6116 (2013).
Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." Proceedings of the 2nd International Conference on Learning Representations. (2014).
Clevert, Djork-Arné, Thomas Unterthiner, and Sepp Hochreiter. "Fast and accurate deep network learning by exponential linear units (elus)." arXiv preprint arXiv:1511.07289 (2015).
Gulcehre, Caglar, et al. "Noisy Activation Functions." Proceedings of The 33rd International Conference on Machine Learning. 2016.
Klambauer, Günter, et al. "Self-Normalizing Neural Networks." arXiv preprint arXiv:1706.02515 (2017).

Optimization Algorithm

Zhang, Jian, Ioannis Mitliagkas, and Christopher Ré. "YellowFin and the Art of Momentum Tuning." arXiv preprint arXiv:1706.03471 (2017).

Vanishing Gradient Problem

Pascanu, Razvan, Tomas Mikolov, and Yoshua Bengio. "On the difficulty of training recurrent neural networks." Proceedings of The 30th International Conference on Machine Learning. 2013.

Oversampling

Chawla, Nitesh V., et al. "SMOTE: synthetic minority over-sampling technique." Journal of artificial intelligence research 16 (2002): 321–357.
Nguyen, Hien M., Eric W. Cooper, and Katsuari Kamei. "Borderline over-sampling for imbalanced data classification." International Journal of Knowledge Engineering and Soft Data Paradigms 3.1 (2011): 4–21.

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