21  References

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259

Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Turing, A. M. (1948). Intelligent machinery. National Physical Laboratory.

Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. Wiley.

Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79–86. https://doi.org/10.1214/aoms/1177729694

Robbins, H., & Monro, S. (1951). A stochastic approximation method. The Annals of Mathematical Statistics, 22(3), 400–407. https://doi.org/10.1214/aoms/1177729586

Farley, B. G., & Clark, W. A. (1954). Simulation of self-organizing systems by digital computer. IRE Transactions on Information Theory, 4(4), 76–84.

Minsky, M. L. (1954). Theory of neural-analog reinforcement systems and its application to the brain-model problem [PhD thesis]. Princeton University.

Rochester, N., Holland, J. H., Habit, L. H., & Duda, W. L. (1956). Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Transactions on Information Theory, 2(3), 80–93.

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408. https://doi.org/10.1037/h0042519

Novikoff, A. B. J. (1962). On convergence proofs on perceptrons. Proceedings of the Symposium on the Mathematical Theory of Automata, 12, 615–622.

Polyak, B. T. (1964). Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5), 1–17. https://doi.org/10.1016/0041-5553(64)90137-5

Minsky, M., & Papert, S. (1969). Perceptrons: An introduction to computational geometry. MIT Press.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0

Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen netzen [PhD thesis]. Technische Universität München.

Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166. https://doi.org/10.1109/72.279181

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018

LeCun, Y., Bottou, L., Orr, G. B., & Müller, K.-R. (1998). Efficient BackProp. In Neural networks: Tricks of the trade (pp. 9–50). Springer. https://doi.org/10.1007/3-540-49430-8_2

Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), 249–256.

Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning (ICML), 1310–1318.