:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Bleistein, Linus, Guilloux, Agathe
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2305.16791
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dynamical Survival Analysis with Controlled Latent States
by: Bleistein, Linus, et al.
Published: (2024)

Optimal Transport with Heterogeneously Missing Data
by: Bleistein, Linus, et al.
Published: (2025)

Optimal Transport under Group Fairness Constraints
by: Bleistein, Linus, et al.
Published: (2026)

Latent Neural-ODE for Model-Informed Precision Dosing: Overcoming Structural Assumptions in Pharmacokinetics
by: Maurel, Benjamin, et al.
Published: (2026)

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

Toward Valid Generative Clinical Trial Data with Survival Endpoints
by: Chassat, Perrine, et al.
Published: (2025)

Causal Evaluation of Membership Inference Attacks
by: Even, Mathieu, et al.
Published: (2026)

KANFormer for Predicting Fill Probabilities via Survival Analysis in Limit Order Books
by: Zhong, Jinfeng, et al.
Published: (2025)

Large Language Models as Markov Chains
by: Zekri, Oussama, et al.
Published: (2024)

Universal Time Series Generation with Neural Controlled Differential Equations
by: Berndt, Torben, et al.
Published: (2026)

Correctness Verification of Neural Networks Approximating Differential Equations
by: Ellinas, Petros, et al.
Published: (2024)

Predicting gene essentiality and drug response from perturbation screens in preclinical cancer models with LEAP: Layered Ensemble of Autoencoders and Predictors
by: Bodinier, Barbara, et al.
Published: (2025)

Neural Controlled Differential Equations with Quantum Hidden Evolutions
by: Yang, Lingyi, et al.
Published: (2024)

Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
by: Hess, Konstantin, et al.
Published: (2023)

Generalization Bound for a General Class of Neural Ordinary Differential Equations
by: Verma, Madhusudan, et al.
Published: (2025)

Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
by: Serov, Egor, et al.
Published: (2026)

Continuously Evolving Graph Neural Controlled Differential Equations for Traffic Forecasting
by: Wu, Jiajia, et al.
Published: (2024)

Random Controlled Differential Equations
by: Piatti, Francesco, et al.
Published: (2025)

Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations
by: Qin, Tiexin, et al.
Published: (2023)

Control and optimization for Neural Partial Differential Equations in Supervised Learning
by: Bensoussan, Alain, et al.
Published: (2025)

Continuum Dropout for Neural Differential Equations
by: Lee, Jonghun, et al.
Published: (2025)

Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference
by: Walker, Benjamin, et al.
Published: (2024)

Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
by: Berndt, Torben, et al.
Published: (2025)

Approximation to Deep Q-Network by Stochastic Delay Differential Equations
by: Lu, Jianya, et al.
Published: (2025)

Qrlew: Rewriting SQL into Differentially Private SQL
by: Grislain, Nicolas, et al.
Published: (2024)

Control, Optimal Transport and Neural Differential Equations in Supervised Learning
by: Phung, Minh-Nhat, et al.
Published: (2025)

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

Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)

Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
by: Lim, Seonkyu, et al.
Published: (2024)

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

Neural Variable-Order Fractional Differential Equation Networks
by: Cui, Wenjun, et al.
Published: (2025)

Variational Neural Stochastic Differential Equations with Change Points
by: El-Laham, Yousef, et al.
Published: (2024)

Neural Differential Appearance Equations
by: Liu, Chen, et al.
Published: (2024)

DInf-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids
by: Kairanda, Navami, et al.
Published: (2026)

Optimizing External Sources for Controlled Burning Plasma in Tokamaks with Neural Ordinary Differential Equations
by: Liu, Zefang, et al.
Published: (2025)

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

Learning Stable and Passive Neural Differential Equations
by: Cheng, Jing, et al.
Published: (2024)

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
by: Furuya, Takashi, et al.
Published: (2024)

ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers
by: Pinos, Michal, et al.
Published: (2024)

Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
by: Miyatake, Luis Kin, et al.
Published: (2025)