| Deep factors for forecasting Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski International Conference on Machine Learning, 6607-6617, 2019 | 34 | 2019 |
| Gluonts: Probabilistic time series models in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 26 | 2019 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240, 2020 | 19 | 2020 |
| Deep factors with gaussian processes for forecasting DC Maddix, Y Wang, A Smola arXiv preprint arXiv:1812.00098, 2018 | 11 | 2018 |
| GluonTS: Probabilistic and Neural Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 10 | 2020 |
| GluonTS: Probabilistic Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 6 | 2019 |
| Numerical artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations DC Maddix, L Sampaio, M Gerritsen Journal of Computational Physics 368, 277-298, 2018 | 4 | 2018 |
| Numerical Artifacts in the Generalized Porous Medium Equation: Why Harmonic Averaging Itself Is Not to Blame D Maddix, M Gerritsen, L Sampaio Journal of Computational Physics 361, 280-298, 2018 | 4 | 2018 |
| Advanced fluid reduced order models for compressible flow I Tezaur, J Fike, K Carlberg, M Barone, D Maddix, E Mussoni, ... Tech. Rep., 2017 | 2 | 2017 |
| Diagnosing malignant versus benign breast tumors via machine learning techniques in high dimensions DC Maddix Stanford Univ., Stanford, CA, USA, Tech. Rep, 2014 | 2 | 2014 |
| Minres: Sparse symmetric equations CC Paige, MA Saunders, SC Choi, D Orban, UE Villa, D Maddix, S Regev | 2 | 2004 |
| Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems R Wang, D Maddix, C Faloutsos, Y Wang, R Yu arXiv preprint arXiv:2011.10616, 2020 | 1 | 2020 |
| Attention-based Domain Adaptation for Time Series Forecasting X Jin, Y Park, D Maddix, Y Wang, X Yan arXiv preprint arXiv:2102.06828, 2021 | | 2021 |
| Learning Dynamical Systems Requires Rethinking Generalization R Wang, D Maddix, C Faloutsos, W Yuyang, R Yu Interpretable Inductive Bias and Physically Structured Learning NeurIPS Workshop, 2020 | | 2020 |
| AutoODE: Bridging Physics-based and Data-driven modeling for COVID-19 Forecasting R Wang, D Maddix, C Faloutsos, Y Wang, R Yu | | 2020 |
| Neural time series models with GluonTS Time Series Workshop ICML 2019 A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... | | 2019 |
| Numerical Artifacts in the Generalized Porous Medium Equation and Solutions DC Maddix Stanford University, 2018 | | 2018 |
| Structure and Parameter Learning in Bayesian Networks with Applications to Predicting Breast Cancer Tumor Malignancy in a Lower Dimension Feature Space D Maddix | | 2016 |
| Investigating the Effects of MINRES with Local Reorthogonalization DC Maddix | | 2015 |
| Using Numerical Optimization Methods to find Hamiltonian Cycle in Directed Graph DC Maddix | | 2014 |
