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Neural SDEs as Infinite-Dimensional GANs
v1v2 (latest)

Neural SDEs as Infinite-Dimensional GANs

International Conference on Machine Learning (ICML), 2021
6 February 2021
Patrick Kidger
James Foster
Xuechen Li
Harald Oberhauser
Terry Lyons
    DiffM
ArXiv (abs)PDFHTML

Papers citing "Neural SDEs as Infinite-Dimensional GANs"

50 / 117 papers shown
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Jian Xu
Zhiqi Lin
Min Chen
Junmei Yang
Delu Zeng
John Paisley
430
0
0
12 Aug 2024
The Stochastic Occupation Kernel Method for System Identification
The Stochastic Occupation Kernel Method for System Identification
Michael L. Wells
Kamel Lahouel
Bruno Jedynak
158
1
0
21 Jun 2024
Probabilistic Temporal Prediction of Continuous Disease Trajectories and
  Treatment Effects Using Neural SDEs
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEsInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024
J. Durso-Finley
Berardino Barile
Jean-Pierre Falet
Douglas L. Arnold
Nick Pawlowski
Tal Arbel
AI4CE
254
3
0
18 Jun 2024
A local squared Wasserstein-2 method for efficient reconstruction of
  models with uncertainty
A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty
Mingtao Xia
Qijing Shen
164
2
0
10 Jun 2024
An efficient Wasserstein-distance approach for reconstructing
  jump-diffusion processes using parameterized neural networks
An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
Mingtao Xia
Xiangting Li
Qijing Shen
Tom Chou
220
8
0
03 Jun 2024
Integrating GNN and Neural ODEs for Estimating Two-Body Interactions in
  Mixed-Species Collective Motion
Integrating GNN and Neural ODEs for Estimating Two-Body Interactions in Mixed-Species Collective Motion
Masahito Uwamichi
S. Schnyder
Tetsuya J. Kobayashi
Satoshi Sawai
153
0
0
26 May 2024
Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough
  Signals
Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
Christian Holberg
C. Salvi
234
9
0
22 May 2024
Forecasting with an N-dimensional Langevin Equation and a
  Neural-Ordinary Differential Equation
Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
Antonio Malpica-Morales
M. Durán-Olivencia
S. Kalliadasis
AI4TS
152
1
0
12 May 2024
Single-seed generation of Brownian paths and integrals for adaptive and high order SDE solvers
Single-seed generation of Brownian paths and integrals for adaptive and high order SDE solvers
Andravz Jelinvcivc
James Foster
Patrick Kidger
409
3
0
10 May 2024
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation
Akshay Thakur
Souvik Chakraborty
300
1
0
24 Apr 2024
Probabilistic Forecasting with Stochastic Interpolants and Föllmer
  Processes
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen
Mark Goldstein
Mengjian Hua
M. S. Albergo
Nicholas M. Boffi
Eric Vanden-Eijnden
AI4TS
366
37
0
20 Mar 2024
Stable Neural Stochastic Differential Equations in Analyzing Irregular
  Time Series Data
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
YongKyung Oh
Dongyoung Lim
Sungil Kim
AI4TS
574
38
0
22 Feb 2024
Squared Wasserstein-2 Distance for Efficient Reconstruction of
  Stochastic Differential Equations
Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations
Mingtao Xia
Xiangting Li
Qijing Shen
Tom Chou
245
5
0
21 Jan 2024
Fixed Point Diffusion Models
Fixed Point Diffusion ModelsComputer Vision and Pattern Recognition (CVPR), 2024
Xingjian Bai
Luke Melas-Kyriazi
241
4
0
16 Jan 2024
Time-changed normalizing flows for accurate SDE modeling
Time-changed normalizing flows for accurate SDE modeling
Naoufal El Bekri
Lucas Drumetz
Franck Vermet
AI4TSBDL
292
0
0
22 Dec 2023
Neural Stochastic Differential Equations with Change Points: A
  Generative Adversarial Approach
Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach
Zhongchang Sun
Yousef El-Laham
Svitlana Vyetrenko
AI4TS
262
2
0
20 Dec 2023
Amortized Reparametrization: Efficient and Scalable Variational
  Inference for Latent SDEs
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEsNeural Information Processing Systems (NeurIPS), 2023
Kevin Course
P. Nair
206
11
0
16 Dec 2023
STDiff: Spatio-temporal Diffusion for Continuous Stochastic Video
  Prediction
STDiff: Spatio-temporal Diffusion for Continuous Stochastic Video Prediction
Xi Ye
Guillaume-Alexandre Bilodeau
VGenDiffM
221
18
0
11 Dec 2023
Stochastic Optimal Control Matching
Stochastic Optimal Control MatchingNeural Information Processing Systems (NeurIPS), 2023
Carles Domingo-Enrich
Jiequn Han
Brandon Amos
Joan Bruna
Ricky T. Q. Chen
DiffM
344
26
0
04 Dec 2023
Tipping Points of Evolving Epidemiological Networks: Machine
  Learning-Assisted, Data-Driven Effective Modeling
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective ModelingChaos (Chaos), 2023
N. Evangelou
Tianqi Cui
J. M. Bello-Rivas
Alexei Makeev
Ioannis G. Kevrekidis
293
3
0
01 Nov 2023
TSGBench: Time Series Generation Benchmark
TSGBench: Time Series Generation BenchmarkProceedings of the VLDB Endowment (PVLDB), 2023
Yihao Ang
Qiang Huang
Yifan Bao
Anthony K. H. Tung
Zhiyong Huang
AI4TS
398
33
0
07 Sep 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe
M. Deisenroth
179
5
0
21 Aug 2023
Generative Modelling of Lévy Area for High Order SDE Simulation
Generative Modelling of Lévy Area for High Order SDE Simulation
Andravz Jelinvcivc
Ji-cheng Tao
William F. Turner
Thomas Cass
James Foster
H. Ni
DiffM
248
4
0
04 Aug 2023
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for
  Probabilistic Time Series Forecasting
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series ForecastingNeural Information Processing Systems (NeurIPS), 2023
Marcel Kollovieh
Abdul Fatir Ansari
Michael Bohlke-Schneider
Jasper Zschiegner
Hao Wang
Yuyang Wang
DiffMAI4TS
316
88
0
21 Jul 2023
Latent SDEs on Homogeneous Spaces
Latent SDEs on Homogeneous SpacesNeural Information Processing Systems (NeurIPS), 2023
Sebastian Zeng
Florian Graf
Roland Kwitt
BDL
371
12
0
28 Jun 2023
Physics-Informed Machine Learning for Modeling and Control of Dynamical
  Systems
Physics-Informed Machine Learning for Modeling and Control of Dynamical SystemsAmerican Control Conference (ACC), 2023
Truong X. Nghiem
Ján Drgoňa
Colin N. Jones
Zoltán Nagy
Roland Schwan
...
J. Paulson
Andrea Carron
Melanie Zeilinger
Wenceslao Shaw-Cortez
D. Vrabie
PINNAI4CE
255
63
0
24 Jun 2023
How to Learn and Generalize From Three Minutes of Data:
  Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential
  Equations
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential EquationsConference on Robot Learning (CoRL), 2023
Franck Djeumou
Cyrus Neary
Ufuk Topcu
DiffM
278
14
0
10 Jun 2023
Global universal approximation of functional input maps on weighted spaces
Global universal approximation of functional input maps on weighted spaces
Christa Cuchiero
Philipp Schmocker
Josef Teichmann
574
27
0
05 Jun 2023
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural
  Stochastic Differential Equations
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential EquationsNeural Information Processing Systems (NeurIPS), 2023
Anudhyan Boral
Z. Y. Wan
Leonardo Zepeda-Núñez
James Lottes
Qing Wang
Yi-fan Chen
John R. Anderson
Fei Sha
AI4CEPINN
230
16
0
01 Jun 2023
Stochastic Bridges as Effective Regularizers for Parameter-Efficient
  Tuning
Stochastic Bridges as Effective Regularizers for Parameter-Efficient TuningAnnual Meeting of the Association for Computational Linguistics (ACL), 2023
Weize Chen
Xu Han
Yankai Lin
Zhiyuan Liu
Maosong Sun
Jie Zhou
94
1
0
28 May 2023
Functional Flow Matching
Functional Flow MatchingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Gavin Kerrigan
Giosue Migliorini
Padhraic Smyth
380
31
0
26 May 2023
Non-adversarial training of Neural SDEs with signature kernel scores
Non-adversarial training of Neural SDEs with signature kernel scoresNeural Information Processing Systems (NeurIPS), 2023
Zacharia Issa
Blanka Horvath
M. Lemercier
C. Salvi
AI4TS
328
39
0
25 May 2023
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Equivariant Neural Simulators for Stochastic Spatiotemporal DynamicsNeural Information Processing Systems (NeurIPS), 2023
Koen Minartz
Y. Poels
Simon Koop
Vlado Menkovski
284
7
0
23 May 2023
PCF-GAN: generating sequential data via the characteristic function of
  measures on the path space
PCF-GAN: generating sequential data via the characteristic function of measures on the path spaceNeural Information Processing Systems (NeurIPS), 2023
Hang Lou
Siran Li
Hao Ni
AI4TS
208
13
0
21 May 2023
The Signature Kernel
The Signature Kernel
Darrick Lee
Harald Oberhauser
548
13
0
08 May 2023
Directed Chain Generative Adversarial Networks
Directed Chain Generative Adversarial NetworksInternational Conference on Machine Learning (ICML), 2023
Ming Min
Ruimeng Hu
Tomoyuki Ichiba
AI4TSAI4CEGAN
251
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0
25 Apr 2023
Generative modeling of time-dependent densities via optimal transport
  and projection pursuit
Generative modeling of time-dependent densities via optimal transport and projection pursuitChaos (Chaos), 2023
Jonah Botvinick-Greenhouse
Yunan Yang
R. Maulik
OT
276
3
0
19 Apr 2023
A Brief Survey on the Approximation Theory for Sequence Modelling
A Brief Survey on the Approximation Theory for Sequence ModellingJournal of Machine Learning (JML), 2023
Hao Jiang
Qianxiao Li
Zhong Li
Shida Wang
AI4TS
260
14
0
27 Feb 2023
Estimating Treatment Effects in Continuous Time with Hidden Confounders
Estimating Treatment Effects in Continuous Time with Hidden Confounders
Defu Cao
James Enouen
Wenshu Fan
CML
264
2
0
19 Feb 2023
New directions in the applications of rough path theory
New directions in the applications of rough path theoryIEEE BITS the Information Theory Magazine (BITS), 2023
Adeline Fermanian
Terry Lyons
James Morrill
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270
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0
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SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models
  for General Order Stochastic Dynamics
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic DynamicsJournal of Computational Physics (JCP), 2023
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C. Daskalakis
P. Atzberger
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206
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0
07 Feb 2023
Using Intermediate Forward Iterates for Intermediate Generator
  Optimization
Using Intermediate Forward Iterates for Intermediate Generator Optimization
Harshit Mishra
Jurijs Nazarovs
Manmohan Dogra
Sathya Ravi
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250
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0
05 Feb 2023
Solving Inverse Physics Problems with Score Matching
Solving Inverse Physics Problems with Score MatchingNeural Information Processing Systems (NeurIPS), 2023
Benjamin Holzschuh
S. Vegetti
Nils Thuerey
DiffM
199
14
0
24 Jan 2023
Deep Latent State Space Models for Time-Series Generation
Deep Latent State Space Models for Time-Series GenerationInternational Conference on Machine Learning (ICML), 2022
Linqi Zhou
Michael Poli
Winnie Xu
Stefano Massaroli
Stefano Ermon
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275
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0
24 Dec 2022
Nonparametric plug-in classifier for multiclass classification of S.D.E.
  paths
Nonparametric plug-in classifier for multiclass classification of S.D.E. pathsScandinavian Journal of Statistics (Scand. J. Stat.), 2022
Christophe Denis
Charlotte Dion‐Blanc
Eddy Ella‐Mintsa
Viet Chi Tran
188
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20 Dec 2022
Neural Langevin Dynamics: towards interpretable Neural Stochastic
  Differential Equations
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations
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M. Peletier
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Vlado Menkovski
DiffM
161
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Modeling Temporal Data as Continuous Functions with Stochastic Process
  Diffusion
Modeling Temporal Data as Continuous Functions with Stochastic Process DiffusionInternational Conference on Machine Learning (ICML), 2022
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Kashif Rasul
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Stephan Günnemann
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330
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Robotic Table Wiping via Reinforcement Learning and Whole-body
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Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
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E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations
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Seongsik Park
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