{"total":64,"rows":[{"id":"2410.13232","title":"Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation","created":"2024-10-17","cited_by_count":126,"area":"wm"},{"id":"2305.14343","title":"Video Prediction Models as Rewards for Reinforcement Learning","created":"2023-05-23","cited_by_count":78,"area":null},{"id":"2505.16394","title":"Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)","created":"2025-05-22","cited_by_count":47,"area":"wm"},{"id":"2312.08533","title":"World Models via Policy-Guided Trajectory Diffusion","created":"2023-12-13","cited_by_count":34,"area":"wm"},{"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":"2208.14197","title":"A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies","created":"2022-08-26","cited_by_count":28,"area":null},{"id":"2310.08348","title":"LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios","created":"2023-10-12","cited_by_count":20,"area":null},{"id":"2208.14226","title":"Unsupervised Representation Learning in Deep Reinforcement Learning: A Review","created":"2022-08-27","cited_by_count":18,"area":null},{"id":"2106.06064","title":"RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting","created":"2021-06-10","cited_by_count":16,"area":null},{"id":"2205.11051","title":"Flow-based Recurrent Belief State Learning for POMDPs","created":"2022-05-23","cited_by_count":16,"area":null},{"id":"2106.10905","title":"Variational multiple shooting for Bayesian ODEs with Gaussian processes","created":"2021-06-21","cited_by_count":15,"area":null},{"id":"2212.12735","title":"An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context","created":"2022-12-24","cited_by_count":15,"area":null},{"id":"2107.08241","title":"High-Accuracy Model-Based Reinforcement Learning, a Survey","created":"2021-07-17","cited_by_count":13,"area":null},{"id":"2210.09256","title":"On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning","created":"2022-10-17","cited_by_count":13,"area":null},{"id":"2409.14216","title":"R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models","created":"2024-09-21","cited_by_count":10,"area":"wm"},{"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":"2102.01606","title":"Structure-preserving Gaussian Process Dynamics","created":"2021-02-02","cited_by_count":9,"area":null},{"id":"2104.04275","title":"GATSBI: Generative Agent-centric Spatio-temporal Object Interaction","created":"2021-04-09","cited_by_count":8,"area":null},{"id":"2205.12550","title":"Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs","created":"2022-05-25","cited_by_count":8,"area":null},{"id":"2306.05637","title":"On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning","created":"2023-06-09","cited_by_count":8,"area":null},{"id":"2302.09921","title":"Free-Form Variational Inference for Gaussian Process State-Space Models","created":"2023-02-20","cited_by_count":7,"area":null},{"id":"2407.10923","title":"OPa-Ma: Text Guided Mamba for 360-degree Image Out-painting","created":"2024-07-15","cited_by_count":7,"area":"ssm"},{"id":"2208.12975","title":"Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data","created":"2022-08-27","cited_by_count":6,"area":null},{"id":"2212.07608","title":"Output-Dependent Gaussian Process State-Space Model","created":"2022-12-15","cited_by_count":6,"area":null},{"id":"2406.01361","title":"Learning to Play Atari in a World of Tokens","created":"2024-06-03","cited_by_count":6,"area":"wm"},{"id":"2109.06807","title":"A Temporal Variational Model for Story Generation","created":"2021-09-14","cited_by_count":5,"area":null},{"id":"2211.00335","title":"Recurrent Neural Networks and Universal Approximation of Bayesian Filters","created":"2022-11-01","cited_by_count":5,"area":null},{"id":"2301.08843","title":"Towards Flexibility and Interpretability of Gaussian Process State-Space Model","created":"2023-01-21","cited_by_count":5,"area":null},{"id":"2301.12528","title":"Sequential Estimation of Gaussian Process-based Deep State-Space Models","created":"2023-01-29","cited_by_count":5,"area":null},{"id":"2312.05910","title":"Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference","created":"2023-12-10","cited_by_count":5,"area":null},{"id":"2104.03946","title":"Learning What To Do by Simulating the Past","created":"2021-04-08","cited_by_count":4,"area":null},{"id":"2206.08883","title":"CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer","created":"2022-06-17","cited_by_count":4,"area":null},{"id":"2107.13349","title":"Self-Supervised Inference in State-Space Models","created":"2021-07-28","cited_by_count":3,"area":null},{"id":"2309.01074","title":"Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models","created":"2023-09-03","cited_by_count":3,"area":null},{"id":"2309.02351","title":"Exact Inference for Continuous-Time Gaussian Process Dynamics","created":"2023-09-05","cited_by_count":3,"area":null},{"id":"2108.00819","title":"Active Learning in Gaussian Process State Space Model","created":"2021-07-30","cited_by_count":2,"area":null},{"id":"2302.13754","title":"Combining Slow and Fast: Complementary Filtering for Dynamics Learning","created":"2023-02-27","cited_by_count":2,"area":null},{"id":"2309.08256","title":"Sampling-Free Probabilistic Deep State-Space Models","created":"2023-09-15","cited_by_count":2,"area":null},{"id":"2503.18309","title":"Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems","created":"2025-03-24","cited_by_count":2,"area":null},{"id":"2602.23050","title":"Latent Matters: Learning Deep State-Space Models","created":"2026-02-26","cited_by_count":2,"area":null},{"id":"2111.01555","title":"Likelihood-Free Inference in State-Space Models with Unknown Dynamics","created":"2021-11-02","cited_by_count":1,"area":null},{"id":"2203.00936","title":"Continual Learning of Multi-modal Dynamics with External Memory","created":"2022-03-02","cited_by_count":1,"area":null},{"id":"2302.11078","title":"Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting","created":"2023-02-22","cited_by_count":1,"area":null},{"id":"2307.06618","title":"Learning IMM Filter Parameters from Measurements using Gradient Descent","created":"2023-07-13","cited_by_count":1,"area":null},{"id":"2510.01089","title":"Double projection for reconstructing dynamical systems: between stochastic and deterministic regimes","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2605.31547","title":"The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2106.12694","title":"Long short-term relevance learning","created":"2021-06-21","cited_by_count":0,"area":null},{"id":"2110.14157","title":"Dream to Explore: Adaptive Simulations for Autonomous Systems","created":"2021-10-27","cited_by_count":0,"area":null},{"id":"2111.08291","title":"Switching Recurrent Kalman Networks","created":"2021-11-16","cited_by_count":0,"area":null},{"id":"2112.03230","title":"Traversing Time with Multi-Resolution Gaussian Process State-Space Models","created":"2021-12-06","cited_by_count":0,"area":null},{"id":"2411.14679","title":"Recursive Gaussian Process State Space Model","created":"2024-11-22","cited_by_count":0,"area":null},{"id":"2505.12544","title":"Alternators With Noise Models","created":"2025-05-18","cited_by_count":0,"area":"diffusion"},{"id":"2511.08231","title":"Real-Time Performance Analysis of Multi-Fidelity Residual Physics-Informed Neural Process-Based State Estimation for Robotic Systems","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2511.20612","title":"Sparse-to-Field Reconstruction via Stochastic Neural Dynamic Mode Decomposition","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2601.04462","title":"Meta-probabilistic Modeling","created":"2026-01-08","cited_by_count":0,"area":null},{"id":"2601.20985","title":"Distributional Active Inference","created":"2026-01-28","cited_by_count":0,"area":"deeprl"},{"id":"2602.00458","title":"LatentTrack: Sequential Weight Generation via Latent Filtering","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2602.12087","title":"Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL","created":"2026-02-12","cited_by_count":0,"area":"deeprl"},{"id":"2603.02935","title":"Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning","created":"2026-03-03","cited_by_count":0,"area":"offlinerl"},{"id":"2604.03523","title":"Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret","created":"2026-04-04","cited_by_count":0,"area":"offlinerl"},{"id":"2605.21805","title":"Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2609.26890","title":"PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2609.28670","title":"Beyond Static Graph World Models: Learning Stochastic Latent Dynamics over Evolving Topologies","created":"2026-09-23","cited_by_count":0,"area":"wm"},{"id":"2609.33112","title":"Simulation-Free Learning of GP-SDEs from Irregular Observations","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}