Fetching the paper…
Reading the bibliography…
We investigate the use of models from the theory of regularity structures as features in machine learning tasks.
Integration of paths—a faithful representation of paths by non-commutative formal power series
K.-T. Chen · 1958
Earlier work this paper cites.
Differential equations driven by rough signals
T. J. Lyons · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Spectral analysis of nonlinear flows
C. W. Rowley · 2009
Earlier work this paper cites.
Uniqueness for the signature of a path of bounded variation and the reduced path group
B. Hambly · 2010
Earlier work this paper cites.
Dynamic mode decomposition of numerical and experimental data
P. J. Schmid · 2010
Earlier work this paper cites.
A kernel two-sample test
A. Gretton · 2012
Earlier work this paper cites.
Sparse arrays of signatures for online character recognition, 2013
B. Graham · 2013
Earlier work this paper cites.
Stochastic equations in infinite dimensions , vol. 152 of Encyclopedia of Mathematics and its Applications
G. Da Prato · 2014
Earlier work this paper cites.
A theory of regularity structures
M. Hairer · 2014
Earlier work this paper cites.
An introduction to computational stochastic PDEs
G. J. Lord · 2014
Earlier work this paper cites.
”Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis”
F. E. Harrell · 2015
Earlier work this paper cites.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
X. Shi · 2015
Earlier work this paper cites.
Deep learning based feature selection for remote sensing scene classification
Q. Zou · 2015
Earlier work this paper cites.
The signature of a rough path: Uniqueness
H. Boedihardjo · 2016
Earlier work this paper cites.
A primer on the signature method in machine learning, 2016
I. Chevyrev · 2016
Cited alongside, same era.
Characteristic functions of measures on geometric rough paths
I. Chevyrev · 2016
Cited alongside, same era.
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
J. Ling · 2016
Cited alongside, same era.
LPSNet: A novel log path signature feature based hand gesture recognition framework
C. Li · 2017
Cited alongside, same era.
Data-driven discovery of partial differential equations
S. H. Rudy · 2017
Cited alongside, same era.
Learning spatial-semantic context with fully convolutional recurrent network for online handwritten chinese text recognition
Kernels for sequentially ordered data
F. J. Kiraly · 2019
Later among the works it cites.
The signature-based model for early detection of sepsis from electronic health records in the intensive care unit
J. Morrill · 2019
Later among the works it cites.
Machine learning for fluid mechanics
S. L. Brunton · 2020
Later among the works it cites.
Extended physics-informed neural networks (XPINNs): a generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations
A. D. Jagtap · 2020
Later among the works it cites.
Optimal Execution with Rough Path Signatures
J. Kalsi · 2020
Later among the works it cites.
Deep Nitsche method: deep Ritz method with essential boundary conditions
Y. Liao · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Xie · 2017
Cited alongside, same era.
A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
I. P. Arribas · 2018
Cited alongside, same era.
Persistence paths and signature features in topological data analysis
I. Chevyrev · 2018
Cited alongside, same era.
Solving high-dimensional partial differential equations using deep learning
J. Han · 2018
Cited alongside, same era.
Neural networks trained to solve differential equations learn general representations
M. Magill · 2018
Cited alongside, same era.
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi · 2018
Cited alongside, same era.
DGM: a deep learning algorithm for solving partial differential equations
J. Sirignano · 2018
Cited alongside, same era.
Later among the works it cites.
Numerical Method for Model-free Pricing of Exotic Derivatives in Discrete Time Using Rough Path Signatures
T. Lyons · 2020
Later among the works it cites.
Learning to solve differential equations across initial conditions
S. Malik · 2020
Later among the works it cites.
M. Gubinelli · 2021
Closest in time.
An augmented Lagrangian deep learning method for variational problems with essential boundary conditions
J. Huang · 2021
Closest in time.
Signature moments to characterize laws of stochastic processes
I. Chevyrev · 2022
Closest in time.
Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs
P. Hu · 2022
Closest in time.
A semigroup method for high dimensional committor functions based on neural network
H. Li · 2022
Closest in time.
Neural stochastic pdes: Resolution-invariant learning of continuous spatiotemporal dynamics
C. Salvi · 2022
Closest in time.
Parameter estimation for rough differential equations
A. Papavasiliou · 2073
Closest in time.