Author:
Horoi Stefan,Geadah Victor,Wolf Guy,Lajoie Guillaume
Publisher
Springer International Publishing
Reference10 articles.
1. Bengio, Y., Mikolov, T., Pascanu, R.: On the difficulty of training Recurrent Neural Networks. arXiv e-prints, November 2012
2. Cohen, U., Chung, S., Lee, D.D., Sompolinsky, H.: Separability and geometry of object manifolds in deep neural networks. bioRxiv (2019)
3. Crisanti, A., Sompolinsky, H.: Path integral approach to random neural networks. Phys. Rev. E 98(6), 062120 (2018)
4. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
5. Marquez, B.A., Larger, L., Jacquot, M., Chembo, Y.K., Brunner, D.: Dynamical complexity and computation in recurrent neural networks beyond their fixed point. Sci. Rep. 8(1), 3319 (2018)
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