热门出版物 - 学术资源搜索

引用“”的文章Neural ordinary differential equations

标题/作者	年份
Denoising diffusion probabilistic models J Ho, A Jain, P Abbeel Advances in neural information processing systems 33, 6840-6851	2020
Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network A Sherstinsky Physica D: Nonlinear Phenomena 404, 132306	2020
Physics-informed machine learning GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang Nature Reviews Physics 3 (6), 422-440	2021
Machine learning for fluid mechanics SL Brunton, BR Noack, P Koumoutsakos Annual review of fluid mechanics 52, 477-508	2020
Score-based generative modeling through stochastic differential equations Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole arXiv preprint arXiv:2011.13456	2020
Implicit neural representations with periodic activation functions V Sitzmann, J Martel, A Bergman, D Lindell, G Wetzstein Advances in neural information processing systems 33, 7462-7473	2020
Denoising diffusion implicit models J Song, C Meng, S Ermon arXiv preprint arXiv:2010.02502	2020
Normalizing flows for probabilistic modeling and inference G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ... The Journal of Machine Learning Research 22 (1), 2617-2680	2021
Normalizing flows: An introduction and review of current methods I Kobyzev, SJD Prince, MA Brubaker IEEE transactions on pattern analysis and machine intelligence 43 (11), 3964 …	2020
Hamiltonian neural networks S Greydanus, M Dzamba, J Yosinski Advances in neural information processing systems 32	2019
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators L Lu, P Jin, G Pang, Z Zhang, GE Karniadakis Nature machine intelligence 3 (3), 218-229	2021
Ffjord: Free-form continuous dynamics for scalable reversible generative models W Grathwohl, RTQ Chen, J Bettencourt, I Sutskever, D Duvenaud arXiv preprint arXiv:1810.01367	2018
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing V Monga, Y Li, YC Eldar IEEE Signal Processing Magazine 38 (2), 18-44	2021
Latent ordinary differential equations for irregularly-sampled time series Y Rubanova, RTQ Chen, DK Duvenaud Advances in neural information processing systems 32	2019
Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... ACM Computing Surveys (CSUR) 55 (2), 1-96	2022
Time-series forecasting with deep learning: a survey B Lim, S Zohren Philosophical Transactions of the Royal Society A 379 (2194), 20200209	2021
Learning deep transformer models for machine translation Q Wang, B Li, T Xiao, J Zhu, C Li, DF Wong, LS Chao arXiv preprint arXiv:1906.01787	2019
Battery lifetime prognostics X Hu, L Xu, X Lin, M Pecht Joule 4 (2), 310-346	2020
Deep equilibrium models S Bai, JZ Kolter, V Koltun Advances in Neural Information Processing Systems 32	2019
Universal differential equations for scientific machine learning C Rackauckas, Y Ma, J Martensen, C Warner, K Zubov, R Supekar, ... arXiv preprint arXiv:2001.04385	2020

1 - 20

计算机程序会自动估算并确定日期和引用次数。