Learning interpretable continuous-time models of latent stochastic
dynamical systems

Learning interpretable continuous-time models of latent stochastic dynamical systems

12 February 2019

Julien Boussard

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Papers citing "Learning interpretable continuous-time models of latent stochastic dynamical systems"

19 / 19 papers shown

Title
Self-supervised contrastive learning performs non-linear system identification Rodrigo González Laiz Tobias Schmidt Steffen Schneider SSL 83 1 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 130 3 0 19 Jul 2024
A projected nonlinear state-space model for forecasting time series signals Christian Donner Anuj Mishra Hideaki Shimazaki AI4TS 61 0 0 22 Nov 2023
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies Tomoharu Iwata Yoshinobu Kawahara AI4TS AI4CE 91 0 0 26 Dec 2022
Mesoscopic modeling of hidden spiking neurons Shuqiao Wang Valentin Schmutz G. Bellec W. Gerstner 48 5 0 26 May 2022
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs Çağatay Yıldız M. Kandemir Barbara Rakitsch 129 12 0 24 May 2022
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series Daniel Kramer P. Bommer Carlo Tombolini G. Koppe Daniel Durstewitz BDL AI4TS AI4CE 150 20 0 04 Nov 2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Jimmy T.H. Smith Scott W. Linderman David Sussillo 119 30 0 01 Nov 2021
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci C. Pandarinath 72 10 0 29 Oct 2021
Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting Armand Jordana Justin Carpentier Ludovic Righetti AI4CE 83 5 0 22 Jun 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 76 16 0 21 Jun 2021
Moment-Based Variational Inference for Stochastic Differential Equations C. Wildner Heinz Koeppl DiffM 45 4 0 01 Mar 2021
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE Ding Zhou Xue-Xin Wei DRL 262 84 0 09 Nov 2020
Point process models for sequence detection in high-dimensional neural spike trains Alex H. Williams Anthony Degleris Yixin Wang Scott W. Linderman AI4TS 65 30 0 10 Oct 2020
Identifying Latent Stochastic Differential Equations Ali Hasan João M. Pereira Sina Farsiu Vahid Tarokh DiffM 72 21 0 12 Jul 2020
Learning Dynamics Models with Stable Invariant Sets Naoya Takeishi Yoshinobu Kawahara 62 18 0 16 Jun 2020
Unifying and generalizing models of neural dynamics during decision-making D. Zoltowski Jonathan W. Pillow Scott W. Linderman 46 8 0 13 Jan 2020
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems Sebastian Kaltenbach P. Koutsourelakis AI4CE 203 35 0 30 Dec 2019
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Mohammad Reza Keshtkaran C. Pandarinath 69 37 0 21 Aug 2019