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1902.04420
Cited By
Learning interpretable continuous-time models of latent stochastic dynamical systems
International Conference on Machine Learning (ICML), 2019
12 February 2019
Lea Duncker
G. Bohner
Julien Boussard
M. Sahani
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Papers citing
"Learning interpretable continuous-time models of latent stochastic dynamical systems"
33 / 33 papers shown
Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
Manuel Hinz
Maximilian Mauel
Patrick Seifner
David Berghaus
K. Cvejoski
Ramses J. Sanchez
AI4CE
172
2
0
14 Oct 2025
Towards Foundation Inference Models that Learn ODEs In-Context
Maximilian Mauel
Manuel Hinz
Patrick Seifner
David Berghaus
Ramses J. Sanchez
AI4CE
146
3
0
14 Oct 2025
Double projection for reconstructing dynamical systems: between stochastic and deterministic regimes
Viktor Sip
Martin Breyton
S. Petkoski
Viktor Jirsa
189
0
0
01 Oct 2025
SING: SDE Inference via Natural Gradients
Amber Hu
Henry Smith
Scott W. Linderman
DiffM
235
2
0
21 Jun 2025
Self-supervised contrastive learning performs non-linear system identification
International Conference on Learning Representations (ICLR), 2024
Rodrigo González Laiz
Tobias Schmidt
Steffen Schneider
SSL
382
4
0
18 Oct 2024
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
Amber Hu
D. Zoltowski
Aditya Nair
David Anderson
Lea Duncker
Scott W. Linderman
513
14
0
19 Jul 2024
Out-of-Domain Generalization in Dynamical Systems Reconstruction
Niclas Alexander Göring
Florian Hess
Manuel Brenner
Zahra Monfared
Daniel Durstewitz
AI4CE
459
30
0
28 Feb 2024
A projected nonlinear state-space model for forecasting time series signals
International Journal of Forecasting (IJF), 2023
Christian Donner
Anuj Mishra
Hideaki Shimazaki
AI4TS
303
1
0
22 Nov 2023
Inferring Inference
Rajkumar Vasudeva Raju
Zhe Li
Scott W. Linderman
Xaq Pitkow
372
2
0
04 Oct 2023
Trainability, Expressivity and Interpretability in Gated Neural ODEs
International Conference on Machine Learning (ICML), 2023
T. Kim
T. Can
K. Krishnamurthy
AI4CE
464
6
0
12 Jul 2023
Variational Gaussian Process Diffusion Processes
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Prakhar Verma
Vincent Adam
Arno Solin
DiffM
438
10
0
03 Jun 2023
Free-Form Variational Inference for Gaussian Process State-Space Models
International Conference on Machine Learning (ICML), 2023
Xuhui Fan
Edwin V. Bonilla
T. O’Kane
Scott A. Sisson
320
12
0
20 Feb 2023
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies
Tomoharu Iwata
Yoshinobu Kawahara
AI4TS
AI4CE
303
0
0
26 Dec 2022
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
bioRxiv (bioRxiv), 2022
Ran Liu
Mehdi Azabou
M. Dabagia
Jingyun Xiao
Eva L. Dyer
AI4CE
312
27
0
10 Jun 2022
Mesoscopic modeling of hidden spiking neurons
Neural Information Processing Systems (NeurIPS), 2022
Shuqiao Wang
Valentin Schmutz
G. Bellec
W. Gerstner
263
6
0
26 May 2022
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
Neural Information Processing Systems (NeurIPS), 2022
Çağatay Yıldız
M. Kandemir
Barbara Rakitsch
438
14
0
24 May 2022
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
International Conference on Machine Learning (ICML), 2021
Daniel Kramer
P. Bommer
Carlo Tombolini
G. Koppe
Daniel Durstewitz
BDL
AI4TS
AI4CE
320
25
0
04 Nov 2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Neural Information Processing Systems (NeurIPS), 2021
Jimmy T.H. Smith
Scott W. Linderman
David Sussillo
296
38
0
01 Nov 2021
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
Neural Information Processing Systems (NeurIPS), 2021
Feng Zhu
Andrew R. Sedler
Harrison A. Grier
Nauman Ahad
Mark A. Davenport
Matthew T. Kaufman
Andrea Giovannucci
C. Pandarinath
274
11
0
29 Oct 2021
Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting
Armand Jordana
Justin Carpentier
Ludovic Righetti
AI4CE
202
5
0
22 Jun 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Pashupati Hegde
Çağatay Yıldız
Harri Lähdesmäki
Samuel Kaski
Markus Heinonen
408
20
0
21 Jun 2021
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
Neurons, Behavior, Data analysis, and Theory (NBDT), 2021
O. Chehab
Alexandre Défossez
Jean-Christophe Loiseau
Alexandre Gramfort
J. King
AI4TS
257
12
0
03 Mar 2021
Moment-Based Variational Inference for Stochastic Differential Equations
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
C. Wildner
Heinz Koeppl
DiffM
254
6
0
01 Mar 2021
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Ding Zhou
Xue-Xin Wei
DRL
470
102
0
09 Nov 2020
Point process models for sequence detection in high-dimensional neural spike trains
Neural Information Processing Systems (NeurIPS), 2020
Alex H. Williams
Anthony Degleris
Yixin Wang
Scott W. Linderman
AI4TS
168
33
0
10 Oct 2020
Identifying Latent Stochastic Differential Equations
IEEE Transactions on Signal Processing (TSP), 2020
Ali Hasan
João M. Pereira
Sina Farsiu
Vahid Tarokh
DiffM
345
25
0
12 Jul 2020
Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss
Kamil Nar
Yuan Xue
Andrew M. Dai
219
2
0
10 Jul 2020
Learning Dynamics Models with Stable Invariant Sets
Naoya Takeishi
Yoshinobu Kawahara
266
21
0
16 Jun 2020
Unifying and generalizing models of neural dynamics during decision-making
D. Zoltowski
Jonathan W. Pillow
Scott W. Linderman
163
9
0
13 Jan 2020
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Journal of Computational Physics (JCP), 2019
Sebastian Kaltenbach
P. Koutsourelakis
AI4CE
572
40
0
30 Dec 2019
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies
Dominik Schmidt
G. Koppe
Zahra Monfared
Max Beutelspacher
Daniel Durstewitz
AI4CE
397
7
0
08 Oct 2019
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
Neural Information Processing Systems (NeurIPS), 2019
Mohammad Reza Keshtkaran
C. Pandarinath
292
42
0
21 Aug 2019
Learning and Interpreting Potentials for Classical Hamiltonian Systems
Harish S. Bhat
126
4
0
26 Jul 2019
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