This monograph looks at evolving processes in Time-Space. It shows how to develop methods and systems for deep learning and deep knowledge representation in spiking neural networks (SNN), and how this could be used to develop brain-inspired AI systems.
In this tutorial, I will first teach you how to build a recurrent neural network (RNN) with a single layer, consisting of one single neuron, with PyTorch and Google Colab. I will also show you how…
D. Galvin. (2014)cite arxiv:1406.7872Comment: Notes prepared to accompany a series of tutorial lectures given by the author at the 1st Lake Michigan Workshop on Combinatorics and Graph Theory, Western Michigan University, March 15--16 2014.
Z. Wang, und S. Ji. (2018)cite arxiv:1808.08931Comment: The original version was accepted by KDD2018. Code is publicly available at https://github.com/divelab/dilated.
M. Finzi, K. Wang, und A. Wilson. (2020)cite arxiv:2010.13581Comment: NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks.
T. Miyato, S. Maeda, M. Koyama, und S. Ishii. (2017)cite arxiv:1704.03976Comment: To be appeared in IEEE Transactions on Pattern Analysis and Machine Intelligence.