{"total":271,"rows":[{"id":"2209.04747","title":"Diffusion Models in Vision: A Survey","created":"2022-09-10","cited_by_count":1015,"area":"diffusion"},{"id":"2302.00482","title":"Improving and generalizing flow-based generative models with minibatch optimal transport","created":"2023-02-01","cited_by_count":879,"area":"diffusion"},{"id":"2101.09258","title":"Maximum Likelihood Training of Score-Based Diffusion Models","created":"2021-01-22","cited_by_count":672,"area":"diffusion"},{"id":"2205.07460","title":"Diffusion Models for Adversarial Purification","created":"2022-05-16","cited_by_count":476,"area":"diffusion"},{"id":"2302.03660","title":"Flow Matching on General Geometries","created":"2023-02-07","cited_by_count":265,"area":"diffusion"},{"id":"2106.10934","title":"GRAND: Graph Neural Diffusion","created":"2021-06-21","cited_by_count":258,"area":null},{"id":"2106.02808","title":"A Variational Perspective on Diffusion-Based Generative Models and Score Matching","created":"2021-06-05","cited_by_count":183,"area":"diffusion"},{"id":"2111.15141","title":"Path Integral Sampler: a stochastic control approach for sampling","created":"2021-11-30","cited_by_count":181,"area":null},{"id":"2211.01364","title":"An optimal control perspective on diffusion-based generative modeling","created":"2022-11-02","cited_by_count":147,"area":"diffusion"},{"id":"2402.15194","title":"Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control","created":"2024-02-23","cited_by_count":143,"area":"diffusion"},{"id":"2208.07949","title":"Riemannian Diffusion Models","created":"2022-08-16","cited_by_count":122,"area":"diffusion"},{"id":"2111.11344","title":"Modeling Irregular Time Series with Continuous Recurrent Units","created":"2021-11-22","cited_by_count":103,"area":null},{"id":"2210.06662","title":"Action Matching: Learning Stochastic Dynamics from Samples","created":"2022-10-13","cited_by_count":98,"area":null},{"id":"2110.05266","title":"Chaos as an interpretable benchmark for forecasting and data-driven modelling","created":"2021-10-11","cited_by_count":88,"area":null},{"id":"2110.09443","title":"Beltrami Flow and Neural Diffusion on Graphs","created":"2021-10-18","cited_by_count":84,"area":null},{"id":"2110.07579","title":"Diffusion Normalizing Flow","created":"2021-10-14","cited_by_count":77,"area":"diffusion"},{"id":"2106.13898","title":"Closed-form Continuous-time Neural Models","created":"2021-06-25","cited_by_count":65,"area":null},{"id":"2206.14928","title":"Manifold Interpolating Optimal-Transport Flows for Trajectory Inference","created":"2022-06-29","cited_by_count":64,"area":null},{"id":"2209.15090","title":"Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments","created":"2022-09-29","cited_by_count":60,"area":null},{"id":"2103.12413","title":"Neural ODE Processes","created":"2021-03-23","cited_by_count":57,"area":null},{"id":"2310.02619","title":"Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs","created":"2023-10-04","cited_by_count":49,"area":null},{"id":"2110.10249","title":"Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics","created":"2021-10-19","cited_by_count":47,"area":null},{"id":"2106.11028","title":"Neural Controlled Differential Equations for Online Prediction Tasks","created":"2021-06-21","cited_by_count":46,"area":null},{"id":"2310.10649","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","created":"2023-10-16","cited_by_count":46,"area":null},{"id":"2102.04668","title":"MALI: A memory efficient and reverse accurate integrator for Neural ODEs","created":"2021-02-09","cited_by_count":42,"area":null},{"id":"2110.04840","title":"Heavy Ball Neural Ordinary Differential Equations","created":"2021-10-10","cited_by_count":42,"area":null},{"id":"2102.04764","title":"Continuous-Time Model-Based Reinforcement Learning","created":"2021-02-09","cited_by_count":41,"area":null},{"id":"2310.02233","title":"Generalized Schr\\\"odinger Bridge Matching","created":"2023-10-03","cited_by_count":40,"area":null},{"id":"2312.02027","title":"Stochastic Optimal Control Matching","created":"2023-12-04","cited_by_count":40,"area":null},{"id":"2106.09004","title":"Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning","created":"2021-06-10","cited_by_count":39,"area":null},{"id":"2211.02590","title":"Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion","created":"2022-11-04","cited_by_count":39,"area":null},{"id":"2102.06559","title":"Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations","created":"2021-02-12","cited_by_count":38,"area":null},{"id":"2212.12749","title":"Deep Latent State Space Models for Time-Series Generation","created":"2022-12-24","cited_by_count":38,"area":"ssm"},{"id":"2107.10211","title":"Differentiable Annealed Importance Sampling and the Perils of Gradient Noise","created":"2021-07-21","cited_by_count":36,"area":null},{"id":"2311.16124","title":"DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification","created":"2023-10-27","cited_by_count":36,"area":"diffusion"},{"id":"2402.14989","title":"Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data","created":"2024-02-22","cited_by_count":36,"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":"2301.11308","title":"Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series","created":"2023-01-26","cited_by_count":29,"area":null},{"id":"2105.04379","title":"Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds","created":"2021-05-10","cited_by_count":27,"area":null},{"id":"2305.07247","title":"Provably Convergent Schr\\\"odinger Bridge with Applications to Probabilistic Time Series Imputation","created":"2023-05-12","cited_by_count":27,"area":"diffusion"},{"id":"2310.17463","title":"Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation","created":"2023-10-26","cited_by_count":26,"area":null},{"id":"2208.05072","title":"Interpretable Polynomial Neural Ordinary Differential Equations","created":"2022-08-09","cited_by_count":24,"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":"2303.01507","title":"Defending against Adversarial Audio via Diffusion Model","created":"2023-03-02","cited_by_count":23,"area":"diffusion"},{"id":"2406.12616","title":"Learning diffusion at lightspeed","created":"2024-06-18","cited_by_count":22,"area":null},{"id":"2106.03885","title":"Differentiable Multiple Shooting Layers","created":"2021-06-07","cited_by_count":20,"area":null},{"id":"2403.20233","title":"Functional Bilevel Optimization for Machine Learning","created":"2024-03-29","cited_by_count":20,"area":null},{"id":"2502.09885","title":"Comprehensive Review of Neural Differential Equations for Time Series Analysis","created":"2025-02-14","cited_by_count":20,"area":null},{"id":"2510.26645","title":"Curly Flow Matching for Learning Non-gradient Field Dynamics","created":"2025-10-30","cited_by_count":20,"area":"diffusion"},{"id":"2501.06148","title":"From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training","created":"2025-01-10","cited_by_count":19,"area":"diffusion"},{"id":"2107.13735","title":"Learning the temporal evolution of multivariate densities via normalizing flows","created":"2021-07-29","cited_by_count":18,"area":null},{"id":"2312.10550","title":"Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs","created":"2023-12-16","cited_by_count":18,"area":null},{"id":"2212.02483","title":"TIDE: Time Derivative Diffusion for Deep Learning on Graphs","created":"2022-12-05","cited_by_count":17,"area":null},{"id":"2502.06999","title":"Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models","created":"2025-02-10","cited_by_count":17,"area":"diffusion"},{"id":"2207.05543","title":"Markovian Gaussian Process Variational Autoencoders","created":"2022-07-12","cited_by_count":16,"area":null},{"id":"2306.16248","title":"Latent SDEs on Homogeneous Spaces","created":"2023-06-28","cited_by_count":16,"area":null},{"id":"2407.17033","title":"Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference","created":"2024-07-24","cited_by_count":16,"area":"diffusion"},{"id":"2106.10905","title":"Variational multiple shooting for Bayesian ODEs with Gaussian processes","created":"2021-06-21","cited_by_count":15,"area":null},{"id":"2106.12430","title":"Beyond Predictions in Neural ODEs: Identification and Interventions","created":"2021-06-23","cited_by_count":15,"area":null},{"id":"2406.16749","title":"Inferring stochastic low-rank recurrent neural networks from neural data","created":"2024-06-24","cited_by_count":15,"area":null},{"id":"2411.06311","title":"When are dynamical systems learned from time series data statistically accurate?","created":"2024-11-09","cited_by_count":15,"area":null},{"id":"2504.03461","title":"Conditioning Diffusions Using Malliavin Calculus","created":"2025-04-04","cited_by_count":15,"area":null},{"id":"2110.15739","title":"Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation","created":"2021-10-29","cited_by_count":14,"area":null},{"id":"2311.03309","title":"Neural Structure Learning with Stochastic Differential Equations","created":"2023-11-06","cited_by_count":14,"area":null},{"id":"2503.03262","title":"Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions","created":"2025-03-05","cited_by_count":14,"area":null},{"id":"2103.03385","title":"Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data","created":"2021-03-04","cited_by_count":13,"area":null},{"id":"2106.12718","title":"Sparse Flows: Pruning Continuous-depth Models","created":"2021-06-24","cited_by_count":13,"area":null},{"id":"2410.00345","title":"A Taxonomy of Loss Functions for Stochastic Optimal Control","created":"2024-10-01","cited_by_count":13,"area":null},{"id":"2101.05834","title":"Physics-aware, probabilistic model order reduction with guaranteed stability","created":"2021-01-14","cited_by_count":12,"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":"2308.11567","title":"Low Tensor Rank Learning of Neural Dynamics","created":"2023-08-22","cited_by_count":12,"area":null},{"id":"2312.06486","title":"STDiff: Spatio-temporal Diffusion for Continuous Stochastic Video Prediction","created":"2023-12-11","cited_by_count":12,"area":null},{"id":"2312.15906","title":"Improving Transferability for Cross-domain Trajectory Prediction via Neural Stochastic Differential Equation","created":"2023-12-26","cited_by_count":12,"area":null},{"id":"2102.09750","title":"Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory","created":"2021-02-19","cited_by_count":11,"area":null},{"id":"2306.02066","title":"Variational Gaussian Process Diffusion Processes","created":"2023-06-03","cited_by_count":11,"area":null},{"id":"2309.05812","title":"Interpretable learning of effective dynamics for multiscale systems","created":"2023-09-11","cited_by_count":11,"area":null},{"id":"2410.05592","title":"Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methods","created":"2024-10-08","cited_by_count":11,"area":null},{"id":"2505.11823","title":"Variational Regularized Unbalanced Optimal Transport: Single Network, Least Action","created":"2025-05-17","cited_by_count":11,"area":null},{"id":"2508.04351","title":"Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points","created":"2025-08-06","cited_by_count":11,"area":"diffusion"},{"id":"2105.08449","title":"Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme","created":"2021-05-18","cited_by_count":10,"area":null},{"id":"2106.15580","title":"Continuous Latent Process Flows","created":"2021-06-29","cited_by_count":10,"area":null},{"id":"2307.14442","title":"Neural Schr\\\"{o}dinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly","created":"2023-07-26","cited_by_count":10,"area":null},{"id":"2410.05602","title":"Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series","created":"2024-10-08","cited_by_count":10,"area":null},{"id":"2108.04941","title":"Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoders","created":"2021-08-10","cited_by_count":9,"area":null},{"id":"2301.13636","title":"Transport with Support: Data-Conditional Diffusion Bridges","created":"2023-01-31","cited_by_count":9,"area":null},{"id":"2410.11648","title":"Efficient, Accurate and Stable Gradients for Neural ODEs","created":"2024-10-15","cited_by_count":9,"area":null},{"id":"2506.01502","title":"Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme","created":"2025-06-02","cited_by_count":9,"area":null},{"id":"2104.01122","title":"M3L: Language-based Video Editing via Multi-Modal Multi-Level Transformers","created":"2021-04-02","cited_by_count":8,"area":null},{"id":"2109.14881","title":"Extracting stochastic dynamical systems with $\\alpha$-stable L\\'evy noise from data","created":"2021-09-30","cited_by_count":8,"area":null},{"id":"2402.14475","title":"DynGMA: a robust approach for learning stochastic differential equations from data","created":"2024-02-22","cited_by_count":8,"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":"2106.11581","title":"Continuous-Depth Neural Models for Dynamic Graph Prediction","created":"2021-06-22","cited_by_count":7,"area":null},{"id":"2201.13114","title":"An end-to-end deep learning approach for extracting stochastic dynamical systems with $\\alpha$-stable L\\'evy noise","created":"2022-01-31","cited_by_count":7,"area":null},{"id":"2204.04853","title":"Neural Lagrangian Schr\\\"odinger Bridge: Diffusion Modeling for Population Dynamics","created":"2022-04-11","cited_by_count":7,"area":"diffusion"},{"id":"2305.15574","title":"Deep Stochastic Processes via Functional Markov Transition Operators","created":"2023-05-24","cited_by_count":7,"area":null},{"id":"2307.01817","title":"Human Trajectory Forecasting with Explainable Behavioral Uncertainty","created":"2023-07-04","cited_by_count":7,"area":null},{"id":"2403.01371","title":"eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling","created":"2024-03-03","cited_by_count":7,"area":null},{"id":"2405.15020","title":"AdjointDEIS: Efficient Gradients for Diffusion Models","created":"2024-05-23","cited_by_count":7,"area":"diffusion"},{"id":"2502.19049","title":"In-Context Learning of Stochastic Differential Equations with Foundation Inference Models","created":"2025-02-26","cited_by_count":7,"area":null},{"id":"2103.00988","title":"Moment-Based Variational Inference for Stochastic Differential Equations","created":"2021-03-01","cited_by_count":6,"area":null}],"next_cursor":"100"}