Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses. - PythonNumericalDemos/Interactive_Gibbs_Sampler.ipynb at master · GeostatsGuy/PythonNumericalDemos
M. Finzi, K. Wang, and A. Wilson. (2020)cite arxiv:2010.13581Comment: NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks.
Y. Yang, I. Morillo, and T. Hospedales. (2018)cite arxiv:1806.06988Comment: presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden.
Z. Wang, and S. Ji. (2018)cite arxiv:1808.08931Comment: The original version was accepted by KDD2018. Code is publicly available at https://github.com/divelab/dilated.
T. Miyato, S. Maeda, M. Koyama, and S. Ishii. (2017)cite arxiv:1704.03976Comment: To be appeared in IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.