{"total":65,"rows":[{"id":"1911.03393","title":"Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models","created":"2019-11-08","cited_by_count":178,"area":"moe"},{"id":"2102.07501","title":"Annealed Flow Transport Monte Carlo","created":"2021-02-15","cited_by_count":90,"area":null},{"id":"2201.13117","title":"Continual Repeated Annealed Flow Transport Monte Carlo","created":"2022-01-31","cited_by_count":66,"area":null},{"id":"2102.07850","title":"Differentiable Particle Filtering via Entropy-Regularized Optimal Transport","created":"2021-02-15","cited_by_count":59,"area":null},{"id":"2210.07992","title":"A Variational Perspective on Generative Flow Networks","created":"2022-10-14","cited_by_count":39,"area":null},{"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":"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":"2206.05952","title":"SIXO: Smoothing Inference with Twisted Objectives","created":"2022-06-13","cited_by_count":22,"area":null},{"id":"2107.08577","title":"Structured World Belief for Reinforcement Learning in POMDP","created":"2021-07-19","cited_by_count":21,"area":"wm"},{"id":"2102.11086","title":"Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding","created":"2021-02-22","cited_by_count":19,"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":"2106.06064","title":"RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting","created":"2021-06-10","cited_by_count":16,"area":null},{"id":"2305.19414","title":"Efficient Training of Energy-Based Models Using Jarzynski Equality","created":"2023-05-30","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":"2106.11302","title":"Nested Variational Inference","created":"2021-06-21","cited_by_count":14,"area":null},{"id":"2112.12194","title":"Surrogate Likelihoods for Variational Annealed Importance Sampling","created":"2021-12-22","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":"2210.05448","title":"A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning","created":"2022-10-11","cited_by_count":12,"area":null},{"id":"2106.00075","title":"Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference","created":"2021-05-31","cited_by_count":11,"area":null},{"id":"2106.13746","title":"On Incorporating Inductive Biases into VAEs","created":"2021-06-25","cited_by_count":11,"area":null},{"id":"2111.01058","title":"Learning to Assimilate in Chaotic Dynamical Systems","created":"2021-11-01","cited_by_count":10,"area":null},{"id":"2403.01499","title":"Normalizing Flow-based Differentiable Particle Filters","created":"2024-03-03","cited_by_count":10,"area":null},{"id":"2403.10610","title":"Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference","created":"2024-03-15","cited_by_count":7,"area":null},{"id":"2103.00668","title":"Learning Proposals for Probabilistic Programs with Inference Combinators","created":"2021-03-01","cited_by_count":6,"area":null},{"id":"2109.15134","title":"Variational Marginal Particle Filters","created":"2021-09-30","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":"2305.01773","title":"Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems","created":"2023-05-02","cited_by_count":5,"area":null},{"id":"2312.12616","title":"Online Variational Sequential Monte Carlo","created":"2023-12-19","cited_by_count":5,"area":null},{"id":"2308.14864","title":"NAS-X: Neural Adaptive Smoothing via Twisting","created":"2023-08-28","cited_by_count":4,"area":null},{"id":"2103.01030","title":"An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations","created":"2021-02-25","cited_by_count":3,"area":null},{"id":"2201.10989","title":"Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives","created":"2022-01-26","cited_by_count":3,"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":"2302.13918","title":"U-Statistics for Importance-Weighted Variational Inference","created":"2023-02-27","cited_by_count":3,"area":null},{"id":"2310.09194","title":"Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling","created":"2023-10-13","cited_by_count":3,"area":null},{"id":"2511.14220","title":"Twice Sequential Monte Carlo for Tree Search","created":"2025-11-18","cited_by_count":3,"area":"deeprl"},{"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":"2304.14390","title":"Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers","created":"2023-04-27","cited_by_count":2,"area":null},{"id":"2309.14612","title":"Reparameterized Variational Rejection Sampling","created":"2023-09-26","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":"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":"2501.05982","title":"Deep Variational Sequential Monte Carlo for High-Dimensional Observations","created":"2025-01-10","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":"2105.09801","title":"Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series","created":"2021-05-20","cited_by_count":1,"area":null},{"id":"2209.13226","title":"Optimization of Annealed Importance Sampling Hyperparameters","created":"2022-09-27","cited_by_count":1,"area":null},{"id":"2210.04023","title":"Multi-Task Dynamical Systems","created":"2022-10-08","cited_by_count":1,"area":null},{"id":"2305.11022","title":"Massively Parallel Reweighted Wake-Sleep","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2406.03242","title":"Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2411.02217","title":"Recursive Learning of Asymptotic Variational Objectives","created":"2024-11-04","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":"2605.31547","title":"The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2103.15343","title":"Variational Rejection Particle Filtering","created":"2021-03-29","cited_by_count":0,"area":null},{"id":"2104.12053","title":"Deep Probabilistic Graphical Modeling","created":"2021-04-25","cited_by_count":0,"area":null},{"id":"2112.05343","title":"Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning","created":"2021-12-10","cited_by_count":0,"area":null},{"id":"2402.16639","title":"Differentiable Particle Filtering using Optimal Placement Resampling","created":"2024-02-26","cited_by_count":0,"area":null},{"id":"2407.16623","title":"Inverse Particle Filter","created":"2024-07-23","cited_by_count":0,"area":null},{"id":"2506.07843","title":"Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels","created":"2025-06-09","cited_by_count":0,"area":null},{"id":"2605.21108","title":"Efficient Learning of Deep State Space Models via Importance Smoothing","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2606.21036","title":"Diffusion-Driven State Space Models","created":"2026-06-19","cited_by_count":0,"area":"diffusion"},{"id":"2607.01012","title":"Generative Model Proposal based Particle Filtering for Data Assimilation","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2609.30498","title":"Learning to Bias: Machine Learning-Enhanced Particle Filters","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2610.16736","title":"Amortised adaptive importance sampling","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}