закладки (спрятать)616
- Generative Models
- CVXPY creates powerful new PyTorch and TensorFlow layers
- 10-414/714: Deep Learning Systems
- Stochastic Adaptive Neural Architecture Search. Contribute to TomVeniat/SANAS development by creating an account on GitHub.
- Deep learning course CE7454, 2019. Contribute to xbresson/CE7454_2019 development by creating an account on GitHub.
- An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (NeurIPS 2019) by Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, and Joseph J. Lim - shaohua0116/MMAML-Classification
- Bayesian Deep Learning Benchmarks. Contribute to OATML/bdl-benchmarks development by creating an account on GitHub.
- Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 Notebook: http://deeplearning.cs.cmu.edu/document/recitation/recitation...
публикации (спрятать)39
- (2019)cite arxiv:1906.00910.
- (2016)cite arxiv:1606.04838.
- (2018)cite arxiv:1801.01944.
- CoRR, (2018)cite arxiv:1810.05095Comment: To appear on Nature Reviews Physics. The revised accepted version will be posted 6 months after publication.
- CoRR, (2018)
- Transactions of the International Society for Music Information Retrieval, 6 (1): 89–102 (2023)
- (2020)cite arxiv:2010.13581Comment: NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks.
- (2019)cite arxiv:1910.05929Comment: 10 pages, 8 figures.
- Information, 14 (2): 137 (февраля 2023)
- (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.
- Scientific Reports, 8 (1): 8912-- (2018)
- (2019)cite arxiv:1909.10893.
- (2019)cite arxiv:1911.05722Comment: Technical report.
- (2015)cite arxiv:1508.04306Comment: Originally submitted on June 5, 2015.
- (2019)cite arxiv:1908.04351.
- CoRR, (2018)
- ICML, том 80 из JMLR Workshop and Conference Proceedings, стр. 2673-2682. JMLR.org, (2018)
- (2020)cite arxiv:2002.02065Comment: 5 pages.
- (2019)cite arxiv:1910.06711.
- (2018)cite arxiv:1808.07804.
- (2020)
- (2021)cite arxiv:2110.05838Comment: Under review.
- (2017)cite arxiv:1704.03976Comment: To be appeared in IEEE Transactions on Pattern Analysis and Machine Intelligence.
- (2016)cite arxiv:1605.07725Comment: Published as a conference paper at ICLR 2017.
- (2019)cite arxiv:1906.10652Comment: 59 pages, under review.
- (2019)cite arxiv:1906.02506Comment: NeurIPS 2019.
- (2019)cite arxiv:1906.02506Comment: Under review.
- CoRR, (2018)
- (2019)cite arxiv:1903.10346Comment: International Conference on Machine Learning (ICML), 2019.
- (2014)cite arxiv:1502.05988.
- (2019)cite arxiv:1902.08295.
- (2019)cite arxiv:1905.00780.
- (2018)cite arxiv:1807.11094Comment: 18 pages, 3 figures, 8 tables.
- (2018)cite arxiv:1808.08931Comment: The original version was accepted by KDD2018. Code is publicly available at https://github.com/divelab/dilated.
- (2018)cite arxiv:1804.06035Comment: 11 pages, 3 figures. Accepted to NAACL 2018.
- Frontiers in Neuroscience, (марта 2020)
- (2019)cite arxiv:1911.04252.
- (2018)cite arxiv:1806.06988Comment: presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden.
- (2020)cite arxiv:2010.10504Comment: 11 pages, 3 figures, 5 tables. Submitted to NeurIPS SAS 2020 Workshop.
