Cover Image

Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Aslanimoghanloo, Muhammad, ElGazzar, Ahmed, van Gerven, Marcel
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations
by: ElGazzar, Ahmed, et al.
Published: (2024)

Probabilistic Forecasting via Autoregressive Flow Matching
by: ElGazzar, Ahmed, et al.
Published: (2025)

Universal Differential Equations as a Common Modeling Language for Neuroscience
by: ElGazzar, Ahmed, et al.
Published: (2024)

Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
by: Oh, YongKyung, et al.
Published: (2024)

Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning
by: Huebotter, Justus, et al.
Published: (2025)

Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning
by: Küçükoğlu, Burcu, et al.
Published: (2025)

Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching
by: Rogalla, Nicole, et al.
Published: (2026)

Noise-based reward-modulated learning
by: Fernández, Jesús García, et al.
Published: (2025)

Comprehensive Review of Neural Differential Equations for Time Series Analysis
by: Oh, YongKyung, et al.
Published: (2025)

Time Series Clustering with General State Space Models via Stochastic Variational Inference
by: Ishizuka, Ryoichi, et al.
Published: (2024)

SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning
by: EskandariNasab, MohammadReza, et al.
Published: (2024)

Neural Structure Learning with Stochastic Differential Equations
by: Wang, Benjie, et al.
Published: (2023)

Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
by: Xu, Jian, et al.
Published: (2024)

TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
by: Oh, YongKyung, et al.
Published: (2025)

Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
by: Xue, Kaiwen, et al.
Published: (2024)

Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting
by: Liu, Yuansan, et al.
Published: (2024)

Neural Laplace for learning Stochastic Differential Equations
by: Carrel, Adrien
Published: (2024)

EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
by: Zhou, Xinxing, et al.
Published: (2026)

Unraveling Text Generation in LLMs: A Stochastic Differential Equation Approach
by: Zhang, Yukun
Published: (2024)

Generative Modeling of Networked Time-Series via Transformer Architectures
by: Elnady, Yusuf
Published: (2025)

Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
by: Küçükoğlu, Burcu, et al.
Published: (2022)

Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
by: Xu, Fred, et al.
Published: (2025)

Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models
by: Major, Noam, et al.
Published: (2026)

Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing
by: Cho, Eunbyeol, et al.
Published: (2025)

Time Series Domain Adaptation via Latent Invariant Causal Mechanism
by: Cai, Ruichu, et al.
Published: (2025)

Latent Laplace Diffusion for Irregular Multivariate Time Series
by: You, Zinuo, et al.
Published: (2026)

ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
by: Hermary, Romain, et al.
Published: (2026)

Trajectory Flow Matching with Applications to Clinical Time Series Modeling
by: Zhang, Xi, et al.
Published: (2024)

Weighted Stochastic Differential Equation to Implement Wasserstein-Fisher-Rao Gradient Flow
by: Rahimi, Herlock
Published: (2025)

A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
by: Liu, Shengchao, et al.
Published: (2023)

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
by: Santamaria-Valenzuela, Inmaculada, et al.
Published: (2025)

VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
by: Zhang, Xudong, et al.
Published: (2026)

Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation
by: Liang, Guojun, et al.
Published: (2024)

ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation
by: EskandariNasab, MohammadReza, et al.
Published: (2024)

AVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time Series Generation
by: EskandariNasab, MohammadReza, et al.
Published: (2025)

Capture Timing-Attention of Events in Clinical Time Series
by: Li, Jia, et al.
Published: (2026)

Hierarchical Time Series Forecasting Via Latent Mean Encoding
by: Salatiello, Alessandro, et al.
Published: (2025)

Efficient CNN-LSTM based Parameter Estimation of Levy Driven Stochastic Differential Equations
by: Li, Shuaiyu, et al.
Published: (2024)

A Comparative Study on How Data Normalization Affects Zero-Shot Generalization in Time Series Foundation Models
by: Ahmed, Ihab, et al.
Published: (2025)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)