{"total":59,"rows":[{"id":"2201.13117","title":"Continual Repeated Annealed Flow Transport Monte Carlo","created":"2022-01-31","cited_by_count":66,"area":null},{"id":"2105.07593","title":"Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation","created":"2021-05-17","cited_by_count":34,"area":null},{"id":"2210.08572","title":"Automatic Differentiation of Programs with Discrete Randomness","created":"2022-10-16","cited_by_count":29,"area":null},{"id":"2107.07687","title":"Auto-differentiable Ensemble Kalman Filters","created":"2021-07-16","cited_by_count":28,"area":null},{"id":"2302.09639","title":"An overview of differentiable particle filters for data-adaptive sequential Bayesian inference","created":"2023-02-19","cited_by_count":23,"area":null},{"id":"2110.13549","title":"Online Variational Filtering and Parameter Learning","created":"2021-10-26","cited_by_count":19,"area":null},{"id":"2106.10314","title":"Differentiable Particle Filtering without Modifying the Forward Pass","created":"2021-06-18","cited_by_count":18,"area":null},{"id":"2107.00488","title":"Differentiable Particle Filters through Conditional Normalizing Flow","created":"2021-07-01","cited_by_count":15,"area":null},{"id":"2206.07630","title":"Rethinking Initialization of the Sinkhorn Algorithm","created":"2022-06-15","cited_by_count":14,"area":null},{"id":"2301.11961","title":"Reduced-Order Autodifferentiable Ensemble Kalman Filters","created":"2023-01-27","cited_by_count":14,"area":null},{"id":"2108.08129","title":"Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure","created":"2021-08-18","cited_by_count":11,"area":null},{"id":"2403.01499","title":"Normalizing Flow-based Differentiable Particle Filters","created":"2024-03-03","cited_by_count":10,"area":null},{"id":"2111.01409","title":"Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters","created":"2021-11-02","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":"2112.09456","title":"Compositional Learning-based Planning for Vision POMDPs","created":"2021-12-17","cited_by_count":8,"area":null},{"id":"2302.01174","title":"Unsupervised Learning of Sampling Distributions for Particle Filters","created":"2023-02-02","cited_by_count":8,"area":null},{"id":"2109.15134","title":"Variational Marginal Particle Filters","created":"2021-09-30","cited_by_count":6,"area":null},{"id":"2203.08617","title":"Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow","created":"2022-03-16","cited_by_count":6,"area":null},{"id":"2307.13147","title":"Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework","created":"2023-07-24","cited_by_count":6,"area":null},{"id":"2209.15609","title":"$\\Phi$-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation","created":"2022-09-30","cited_by_count":5,"area":null},{"id":"2307.03210","title":"Sparse Graphical Linear Dynamical Systems","created":"2023-07-06","cited_by_count":4,"area":null},{"id":"2310.19608","title":"On Feynman--Kac training of partial Bayesian neural networks","created":"2023-10-30","cited_by_count":4,"area":null},{"id":"2407.17296","title":"Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals","created":"2024-07-24","cited_by_count":4,"area":null},{"id":"2502.10546","title":"Learning to be Smooth: An End-to-End Differentiable Particle Smoother","created":"2025-02-14","cited_by_count":4,"area":null},{"id":"2505.16732","title":"Sequential Monte Carlo for Policy Optimization in Continuous POMDPs","created":"2025-05-22","cited_by_count":4,"area":null},{"id":"2212.06968","title":"Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving","created":"2022-12-14","cited_by_count":3,"area":null},{"id":"2202.02264","title":"De-Sequentialized Monte Carlo: a parallel-in-time particle smoother","created":"2022-02-04","cited_by_count":2,"area":null},{"id":"2302.10319","title":"Differentiable Bootstrap Particle Filters for Regime-Switching Models","created":"2023-02-20","cited_by_count":2,"area":null},{"id":"2304.14390","title":"Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers","created":"2023-04-27","cited_by_count":2,"area":null},{"id":"2312.05955","title":"Learning Differentiable Particle Filter on the Fly","created":"2023-12-10","cited_by_count":2,"area":null},{"id":"2312.14000","title":"Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing","created":"2023-12-21","cited_by_count":2,"area":null},{"id":"2404.08789","title":"Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes","created":"2024-04-12","cited_by_count":2,"area":null},{"id":"2404.13698","title":"Resampling-free Particle Filters in High-dimensions","created":"2024-04-21","cited_by_count":2,"area":null},{"id":"2405.01251","title":"Revisiting semi-supervised training objectives for differentiable particle filters","created":"2024-05-02","cited_by_count":2,"area":null},{"id":"2405.04865","title":"Regime Learning for Differentiable Particle Filters","created":"2024-05-08","cited_by_count":2,"area":null},{"id":"2406.08399","title":"Differentiable Cost-Parameterized Monge Map Estimators","created":"2024-06-12","cited_by_count":2,"area":null},{"id":"2501.05982","title":"Deep Variational Sequential Monte Carlo for High-Dimensional Observations","created":"2025-01-10","cited_by_count":2,"area":null},{"id":"2506.17197","title":"Schr\\\"odinger Bridge Matching for Tree-Structured Costs and Entropic Wasserstein Barycentres","created":"2025-06-20","cited_by_count":2,"area":null},{"id":"2507.15716","title":"DiffPF: Differentiable Particle Filtering with Generative Sampling via Conditional Diffusion Models","created":"2025-07-21","cited_by_count":2,"area":"diffusion"},{"id":"2510.25693","title":"PyDPF: A Python Package for Differentiable Particle Filtering","created":"2025-10-29","cited_by_count":2,"area":null},{"id":"2512.10401","title":"Diffusion differentiable resampling","created":"2025-12-11","cited_by_count":2,"area":null},{"id":"2410.22695","title":"Permutation Invariant Learning with High-Dimensional Particle Filters","created":"2024-10-30","cited_by_count":1,"area":null},{"id":"2412.03271","title":"Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs","created":"2024-12-04","cited_by_count":1,"area":null},{"id":"2503.01274","title":"DnD Filter: Differentiable State Estimation for Dynamic Systems using Diffusion Models","created":"2025-03-03","cited_by_count":1,"area":"diffusion"},{"id":"2507.07461","title":"Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals","created":"2025-07-10","cited_by_count":1,"area":null},{"id":"2402.16639","title":"Differentiable Particle Filtering using Optimal Placement Resampling","created":"2024-02-26","cited_by_count":0,"area":null},{"id":"2407.03085","title":"Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiation","created":"2024-07-03","cited_by_count":0,"area":null},{"id":"2407.16623","title":"Inverse Particle Filter","created":"2024-07-23","cited_by_count":0,"area":null},{"id":"2411.15638","title":"Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks","created":"2024-11-23","cited_by_count":0,"area":null},{"id":"2505.16741","title":"Meta-reinforcement learning with minimum attention","created":"2025-05-22","cited_by_count":0,"area":"deeprl"},{"id":"2508.07037","title":"Differentiable Adaptive Kalman Filtering via Optimal Transport","created":"2025-08-09","cited_by_count":0,"area":null},{"id":"2510.27503","title":"pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements","created":"2025-10-31","cited_by_count":0,"area":null},{"id":"2601.21887","title":"VSE: Variational state estimation of complex model-free process","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.08560","title":"DNS: Data-driven Nonlinear Smoother for Complex Model-free Process","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2606.02251","title":"FW-NKF: Frequency-Weighted Neural Kalman Filters","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2607.01012","title":"Generative Model Proposal based Particle Filtering for Data Assimilation","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2609.11132","title":"How Wrong Can a Good Predictor Be? Diverging Updates with Vanishing Predictive KL","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.25942","title":"Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2609.30498","title":"Learning to Bias: Machine Learning-Enhanced Particle Filters","created":"2026-09-24","cited_by_count":0,"area":null}],"next_cursor":null}