{"total":35,"rows":[{"id":"2105.13493","title":"Efficient and Accurate Gradients for Neural SDEs","created":"2021-05-27","cited_by_count":71,"area":null},{"id":"2310.02679","title":"Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization","created":"2023-10-04","cited_by_count":57,"area":null},{"id":"2111.10510","title":"Bayesian Learning via Neural Schr\\\"odinger-F\\\"ollmer Flows","created":"2021-11-20","cited_by_count":56,"area":null},{"id":"2111.01495","title":"Constructing Neural Network-Based Models for Simulating Dynamical Systems","created":"2021-11-02","cited_by_count":31,"area":null},{"id":"2310.17463","title":"Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation","created":"2023-10-26","cited_by_count":26,"area":null},{"id":"2202.11987","title":"Predicting the impact of treatments over time with uncertainty aware neural differential equations","created":"2022-02-24","cited_by_count":23,"area":null},{"id":"2307.01050","title":"Transport meets Variational Inference: Controlled Monte Carlo Diffusions","created":"2023-07-03","cited_by_count":21,"area":null},{"id":"2202.12278","title":"Learning Stochastic Dynamics with Statistics-Informed Neural Network","created":"2022-02-24","cited_by_count":16,"area":null},{"id":"2302.06584","title":"Thermodynamic AI and the fluctuation frontier","created":"2023-02-09","cited_by_count":15,"area":null},{"id":"2106.10820","title":"Stateful ODE-Nets using Basis Function Expansions","created":"2021-06-21","cited_by_count":14,"area":null},{"id":"2306.01174","title":"Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations","created":"2023-06-01","cited_by_count":13,"area":null},{"id":"2107.08467","title":"GoTube: Scalable Stochastic Verification of Continuous-Depth Models","created":"2021-07-18","cited_by_count":12,"area":null},{"id":"2209.10091","title":"Variational Inference for Infinitely Deep Neural Networks","created":"2022-09-21","cited_by_count":10,"area":null},{"id":"2306.06335","title":"How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations","created":"2023-06-10","cited_by_count":10,"area":null},{"id":"2408.16115","title":"Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations","created":"2024-08-28","cited_by_count":8,"area":null},{"id":"2402.03495","title":"Partially Stochastic Infinitely Deep Bayesian Neural Networks","created":"2024-02-05","cited_by_count":6,"area":null},{"id":"2410.13563","title":"Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines","created":"2024-10-17","cited_by_count":6,"area":null},{"id":"2310.09434","title":"Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations","created":"2023-10-13","cited_by_count":5,"area":null},{"id":"2305.15557","title":"Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence","created":"2023-05-24","cited_by_count":4,"area":null},{"id":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2408.06069","title":"Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations","created":"2024-08-12","cited_by_count":4,"area":null},{"id":"2308.10644","title":"Faster Training of Neural ODEs Using Gau{\\ss}-Legendre Quadrature","created":"2023-08-21","cited_by_count":3,"area":null},{"id":"2308.12316","title":"Graph Neural Stochastic Differential Equations","created":"2023-08-23","cited_by_count":2,"area":null},{"id":"2504.14854","title":"Uncertainty quantification of neural network models of evolving processes via Langevin sampling","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2106.11609","title":"Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models","created":"2021-06-22","cited_by_count":1,"area":null},{"id":"2111.13207","title":"Characteristic Neural Ordinary Differential Equations","created":"2021-11-25","cited_by_count":1,"area":null},{"id":"2205.11989","title":"Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings","created":"2022-05-24","cited_by_count":1,"area":null},{"id":"2512.20272","title":"HGAN-SDEs: Learning Neural Stochastic Differential Equations with Hermite-Guided Adversarial Training","created":"2025-12-23","cited_by_count":1,"area":null},{"id":"2206.08237","title":"Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening","created":"2022-06-16","cited_by_count":0,"area":null},{"id":"2209.00173","title":"Continuous-time Particle Filtering for Latent Stochastic Differential Equations","created":"2022-09-01","cited_by_count":0,"area":null},{"id":"2311.04014","title":"A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems","created":"2023-11-07","cited_by_count":0,"area":null},{"id":"2412.17499","title":"Improving the Noise Estimation of Latent Neural Stochastic Differential Equations","created":"2024-12-23","cited_by_count":0,"area":null},{"id":"2508.17090","title":"Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling","created":"2025-08-23","cited_by_count":0,"area":null},{"id":"2603.25024","title":"Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2604.17954","title":"Complex normalizing flows can almost be information K\\\"ahler-Ricci flows","created":"2026-04-20","cited_by_count":0,"area":null}],"next_cursor":null}