Danielle Maddix
Danielle Maddix
Потвърден имейл адрес: stanford.edu
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
Deep factors for forecasting
Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski
International conference on machine learning, 6607-6617, 2019
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ...
ACM Computing Surveys (CSUR), 2018
Deep factors with gaussian processes for forecasting
DC Maddix, Y Wang, A Smola
arXiv preprint arXiv:1812.00098, 2018
Bridging physics-based and data-driven modeling for learning dynamical systems
R Wang, D Maddix, C Faloutsos, Y Wang, R Yu
Learning for Dynamics and Control, 385-398, 2021
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Y Park, D Maddix, FX Aubet, K Kan, J Gasthaus, Y Wang
International Conference on Artificial Intelligence and Statistics 151, 8127 …, 2022
Domain adaptation for time series forecasting via attention sharing
X Jin, Y Park, D Maddix, H Wang, Y Wang
International Conference on Machine Learning, 10280-10297, 2022
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
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
Advanced Fluid Reduced Order Models for Compressible Flow.
IK Tezaur, JA Fike, KT Carlberg, MF Barone, D Maddix, EE Mussoni, ...
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2017
Diagnosing malignant versus benign breast tumors via machine learning techniques in high dimensions
DC Maddix
Stanford Univ., Stanford, CA, USA, Tech. Rep, 2014
Minres: Sparse symmetric equations
CC Paige, MA Saunders, SC Choi, D Orban, UE Villa, D Maddix, S Regev
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
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
KA Wang, D Maddix, Y Wang
I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 80-85, 2022
Guiding continuous operator learning through Physics-based boundary constraints
N Saad, G Gupta, S Alizadeh, DC Maddix
arXiv preprint arXiv:2212.07477, 2022
Towards Reverse Causal Inference on Panel Data: Precise Formulation and Challenges
J Zhang, Y Park, DC Maddix, D Roth, B Wang
A causal view on dynamical systems, NeurIPS 2022 workshop, 2022
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
X Zhang, X Jin, K Gopalswamy, G Gupta, Y Park, X Shi, H Wang, ...
NeurIPS'22 Workshop on All Things Attention: Bridging Different Perspectives …, 2022
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow
DC Maddix, N Saad, Y Wang
Machine Learning and the Physical Sciences (MLPS) NeurIPS 2021 Workshop, 2021
AutoODE: Bridging Physics-based and Data-driven modeling for COVID-19 Forecasting
R Wang, D Maddix, C Faloutsos, Y Wang, R Yu
Numerical Artifacts in the Generalized Porous Medium Equation and Solutions
DC Maddix
Stanford University, 2018
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