Imagen de Portada

Variational Neural Stochastic Differential Equations with Change Points

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	El-Laham, Yousef, Sun, Zhongchang, Zhu, Haibei, Balch, Tucker, Vyetrenko, Svitlana
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach
por: Sun, Zhongchang, et al.
Publicado: (2023)

Augment on Manifold: Mixup Regularization with UMAP
por: El-Laham, Yousef, et al.
Publicado: (2023)

A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
por: Zhu, Haibei, et al.
Publicado: (2024)

Privacy-Aware Time Series Synthesis via Public Knowledge Distillation
por: Liu, Penghang, et al.
Publicado: (2025)

Mixup Regularization: A Probabilistic Perspective
por: El-Laham, Yousef, et al.
Publicado: (2025)

LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
por: Fons, Elizabeth, et al.
Publicado: (2025)

Synthetic Data Applications in Finance
por: Potluru, Vamsi K., et al.
Publicado: (2023)

Once Burned, Twice Shy? The Effect of Stock Market Bubbles on Traders that Learn by Experience
por: Zhu, Haibei, et al.
Publicado: (2023)

Defending Diffusion Models Against Membership Inference Attacks via Higher-Order Langevin Dynamics
por: Sterling, Benjamin, et al.
Publicado: (2025)

Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
por: Bamford, Tom, et al.
Publicado: (2023)

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics
por: Samota, Sandeep Kumar, et al.
Publicado: (2026)

Empirical Equilibria in Agent-based Economic systems with Learning agents
por: Dwarakanath, Kshama, et al.
Publicado: (2024)

ABIDES-Economist: Agent-Based Simulator of Economic Systems with Learning Agents
por: Dwarakanath, Kshama, et al.
Publicado: (2024)

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

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

Improving the Noise Estimation of Latent Neural Stochastic Differential Equations
por: Heck, Linus, et al.
Publicado: (2024)

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
por: Shi, Dai, et al.
Publicado: (2026)

Neural Laplace for learning Stochastic Differential Equations
por: Carrel, Adrien
Publicado: (2024)

Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling
por: Dharmakeerthi, Kulunu, et al.
Publicado: (2025)

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
por: Bergna, Richard, et al.
Publicado: (2024)

Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
por: Hong, Sanghyun, et al.
Publicado: (2025)

Transparency as Delayed Observability in Multi-Agent Systems
por: Dwarakanath, Kshama, et al.
Publicado: (2024)

Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets
por: Liu, Penghang, et al.
Publicado: (2022)

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond
por: Shi, Dai, et al.
Publicado: (2025)

Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions
por: Zhang, Jianxin, et al.
Publicado: (2024)

Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
por: Oh, YongKyung, et al.
Publicado: (2025)

Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
por: Song, Yujee, et al.
Publicado: (2024)

Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations
por: Aslanimoghanloo, Muhammad, et al.
Publicado: (2025)

PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edge
por: Sugiura, Keisuke, et al.
Publicado: (2025)

Chaos into Order: Neural Framework for Expected Value Estimation of Stochastic Partial Differential Equations
por: Pétursson, Ísak, et al.
Publicado: (2025)

Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
por: Rajaei, Pedram, et al.
Publicado: (2025)

HGAN-SDEs: Learning Neural Stochastic Differential Equations with Hermite-Guided Adversarial Training
por: Xu, Yuanjian, et al.
Publicado: (2025)

Wiener Chaos Expansion based Neural Operator for Singular Stochastic Partial Differential Equations
por: Shi, Dai, et al.
Publicado: (2026)

Towards Identifiability of Interventional Stochastic Differential Equations
por: Zweig, Aaron, et al.
Publicado: (2025)

Constrained Reinforcement Learning Under Model Mismatch
por: Sun, Zhongchang, et al.
Publicado: (2024)

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

CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
por: Chen, Chuanqi, et al.
Publicado: (2024)

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
por: Schertzer, Adrien, et al.
Publicado: (2024)

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
por: Brogan, Joel, et al.
Publicado: (2024)

Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling
por: Lu, Malinda, et al.
Publicado: (2025)