{"total":156,"rows":[{"id":"2107.10043","title":"KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics","created":"2021-07-21","cited_by_count":132,"area":null},{"id":"2105.08756","title":"Pathdreamer: A World Model for Indoor Navigation","created":"2021-05-18","cited_by_count":118,"area":"wm"},{"id":"2111.11344","title":"Modeling Irregular Time Series with Continuous Recurrent Units","created":"2021-11-22","cited_by_count":103,"area":null},{"id":"2308.07498","title":"DREAMWALKER: Mental Planning for Continuous Vision-Language Navigation","created":"2023-08-14","cited_by_count":85,"area":"wm"},{"id":"2306.02451","title":"For SALE: State-Action Representation Learning for Deep Reinforcement Learning","created":"2023-06-04","cited_by_count":75,"area":null},{"id":"2210.13647","title":"Temporally Disentangled Representation Learning","created":"2022-10-24","cited_by_count":61,"area":null},{"id":"2102.13156","title":"Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling","created":"2021-02-25","cited_by_count":59,"area":null},{"id":"2203.13641","title":"StretchBEV: Stretching Future Instance Prediction Spatially and Temporally","created":"2022-03-25","cited_by_count":47,"area":null},{"id":"2102.05829","title":"Causal Inference for Time series Analysis: Problems, Methods and Evaluation","created":"2021-02-11","cited_by_count":42,"area":null},{"id":"2102.09532","title":"Clockwork Variational Autoencoders","created":"2021-02-18","cited_by_count":42,"area":null},{"id":"2107.08829","title":"Visual Adversarial Imitation Learning using Variational Models","created":"2021-07-16","cited_by_count":42,"area":"offlinerl"},{"id":"2501.16142","title":"Towards General-Purpose Model-Free Reinforcement Learning","created":"2025-01-27","cited_by_count":41,"area":"deeprl"},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2207.02542","title":"Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems","created":"2022-07-06","cited_by_count":36,"area":null},{"id":"2110.15245","title":"From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence","created":"2021-10-28","cited_by_count":35,"area":null},{"id":"2106.07992","title":"Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering","created":"2021-06-15","cited_by_count":31,"area":null},{"id":"2110.05721","title":"Action-Sufficient State Representation Learning for Control with Structural Constraints","created":"2021-10-12","cited_by_count":31,"area":null},{"id":"2106.07278","title":"Which Mutual-Information Representation Learning Objectives are Sufficient for Control?","created":"2021-06-14","cited_by_count":29,"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":"2405.15369","title":"Cross-Domain Policy Adaptation by Capturing Representation Mismatch","created":"2024-05-24","cited_by_count":25,"area":"deeprl"},{"id":"2306.03897","title":"DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup","created":"2023-06-04","cited_by_count":19,"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":"2110.09005","title":"Unsupervised Learned Kalman Filtering","created":"2021-10-18","cited_by_count":17,"area":null},{"id":"2210.03466","title":"Latent Neural ODEs with Sparse Bayesian Multiple Shooting","created":"2022-10-07","cited_by_count":17,"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":"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":"2101.07046","title":"Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models","created":"2021-01-18","cited_by_count":14,"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":"2210.00999","title":"Latent State Marginalization as a Low-cost Approach for Improving Exploration","created":"2022-10-03","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":"2306.09205","title":"Reward-Free Curricula for Training Robust World Models","created":"2023-06-15","cited_by_count":13,"area":"wm"},{"id":"2101.05834","title":"Physics-aware, probabilistic model order reduction with guaranteed stability","created":"2021-01-14","cited_by_count":12,"area":null},{"id":"2110.08607","title":"Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty","created":"2021-10-16","cited_by_count":11,"area":null},{"id":"2112.03899","title":"Information is Power: Intrinsic Control via Information Capture","created":"2021-12-07","cited_by_count":11,"area":null},{"id":"2305.16543","title":"Revisiting Structured Variational Autoencoders","created":"2023-05-25","cited_by_count":11,"area":null},{"id":"2512.09929","title":"Closing the Train-Test Gap in World Models for Gradient-Based Planning","created":"2025-12-10","cited_by_count":11,"area":"wm"},{"id":"2111.01058","title":"Learning to Assimilate in Chaotic Dynamical Systems","created":"2021-11-01","cited_by_count":10,"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":"2102.04801","title":"Automatic variational inference with cascading flows","created":"2021-02-09","cited_by_count":9,"area":null},{"id":"2406.15131","title":"KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty","created":"2024-06-21","cited_by_count":9,"area":"deeprl"},{"id":"2503.10375","title":"Probabilistic Forecasting via Autoregressive Flow Matching","created":"2025-03-13","cited_by_count":9,"area":"diffusion"},{"id":"2506.08460","title":"MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning","created":"2025-06-10","cited_by_count":9,"area":"offlinerl"},{"id":"2104.04275","title":"GATSBI: Generative Agent-centric Spatio-temporal Object Interaction","created":"2021-04-09","cited_by_count":8,"area":null},{"id":"2206.14697","title":"Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios","created":"2022-06-29","cited_by_count":8,"area":null},{"id":"2210.04165","title":"Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems","created":"2022-10-09","cited_by_count":8,"area":null},{"id":"2210.11698","title":"Learning Robust Dynamics through Variational Sparse Gating","created":"2022-10-21","cited_by_count":8,"area":"wm"},{"id":"2210.11779","title":"Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation","created":"2022-10-21","cited_by_count":8,"area":null},{"id":"2310.18534","title":"Multi Time Scale World Models","created":"2023-10-27","cited_by_count":8,"area":"wm"},{"id":"2410.05454","title":"Meta-Dynamical State Space Models for Integrative Neural Data Analysis","created":"2024-10-07","cited_by_count":8,"area":null},{"id":"2602.10743","title":"Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking","created":"2026-02-11","cited_by_count":8,"area":null},{"id":"2103.14184","title":"Deformable Linear Object Prediction Using Locally Linear Latent Dynamics","created":"2021-03-26","cited_by_count":7,"area":null},{"id":"2401.11792","title":"Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations","created":"2024-01-22","cited_by_count":7,"area":"driving"},{"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":"2403.16369","title":"Learning Action-based Representations Using Invariance","created":"2024-03-25","cited_by_count":7,"area":"deeprl"},{"id":"2407.12492","title":"Temporal Test-Time Adaptation with State-Space Models","created":"2024-07-17","cited_by_count":7,"area":null},{"id":"2106.11712","title":"Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting","created":"2021-06-22","cited_by_count":6,"area":null},{"id":"2108.02096","title":"Policy Gradients Incorporating the Future","created":"2021-08-04","cited_by_count":6,"area":null},{"id":"2110.04738","title":"Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models","created":"2021-10-10","cited_by_count":6,"area":null},{"id":"2202.05333","title":"Factored World Models for Zero-Shot Generalization in Robotic Manipulation","created":"2022-02-10","cited_by_count":6,"area":"wm"},{"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":"2210.12418","title":"MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction","created":"2022-10-22","cited_by_count":6,"area":null},{"id":"2109.05928","title":"Vision-based system identification and 3D keypoint discovery using dynamics constraints","created":"2021-09-13","cited_by_count":5,"area":null},{"id":"2109.12561","title":"Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking","created":"2021-09-26","cited_by_count":5,"area":null},{"id":"2112.01641","title":"Hamiltonian latent operators for content and motion disentanglement in image sequences","created":"2021-12-02","cited_by_count":5,"area":null},{"id":"2205.05787","title":"Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models","created":"2022-05-11","cited_by_count":5,"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":"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":"2301.13516","title":"Recurrences reveal shared causal drivers of complex time series","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2312.02019","title":"Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models","created":"2023-12-04","cited_by_count":5,"area":"wm"},{"id":"2409.16824","title":"Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability","created":"2024-09-25","cited_by_count":5,"area":"deeprl"},{"id":"2510.25769","title":"Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions","created":"2025-10-29","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":"2204.10419","title":"Learning Sequential Latent Variable Models from Multimodal Time Series Data","created":"2022-04-21","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":"2305.15189","title":"Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts","created":"2023-05-24","cited_by_count":4,"area":null},{"id":"2403.10123","title":"Regularization-Based Efficient Continual Learning in Deep State-Space Models","created":"2024-03-15","cited_by_count":4,"area":null},{"id":"2404.18988","title":"Markovian Transformers for Informative Language Modeling","created":"2024-04-29","cited_by_count":4,"area":"lr"},{"id":"2107.13349","title":"Self-Supervised Inference in State-Space Models","created":"2021-07-28","cited_by_count":3,"area":null},{"id":"2212.10306","title":"Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting","created":"2022-12-20","cited_by_count":3,"area":null},{"id":"2412.12870","title":"Physically Interpretable World Models via Weakly Supervised Representation Learning","created":"2024-12-17","cited_by_count":3,"area":"wm"},{"id":"2502.16232","title":"Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems","created":"2025-02-22","cited_by_count":3,"area":null},{"id":"2510.05874","title":"MaNGO - Adaptable Graph Network Simulators via Meta-Learning","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2111.01892","title":"Equivariant Deep Dynamical Model for Motion Prediction","created":"2021-11-02","cited_by_count":2,"area":null},{"id":"2112.04809","title":"Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion","created":"2021-12-09","cited_by_count":2,"area":null},{"id":"2205.01179","title":"VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation","created":"2022-05-02","cited_by_count":2,"area":null},{"id":"2306.05256","title":"Unscented Autoencoder","created":"2023-06-08","cited_by_count":2,"area":null},{"id":"2308.00913","title":"The Bayesian Context Trees State Space Model for time series modelling and forecasting","created":"2023-08-02","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":"2404.18896","title":"Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models","created":"2024-04-29","cited_by_count":2,"area":"wm"},{"id":"2407.19371","title":"Deep State-Space Generative Model For Correlated Time-to-Event Predictions","created":"2024-07-28","cited_by_count":2,"area":null},{"id":"2501.02721","title":"Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators","created":"2025-01-06","cited_by_count":2,"area":null},{"id":"2504.19115","title":"Towards Latency-Aware 3D Streaming Perception for Autonomous Driving","created":"2025-04-27","cited_by_count":2,"area":null},{"id":"2510.03614","title":"Neural Bayesian Filtering","created":"2025-10-04","cited_by_count":2,"area":null},{"id":"2510.16004","title":"PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction","created":"2025-10-14","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":"2603.14441","title":"AR-Flow VAE: A Structured Autoregressive Flow Prior Variational Autoencoder for Unsupervised Blind Source Separation","created":"2026-03-15","cited_by_count":2,"area":null},{"id":"2604.25897","title":"Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty","created":"2026-04-28","cited_by_count":2,"area":null},{"id":"2103.00930","title":"Unsupervised dynamic modeling of medical image transformation","created":"2021-03-01","cited_by_count":1,"area":null}],"next_cursor":"100"}