Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:11:16.615572Z
Paper Citation Record · LEDGER
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
As of 14 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2411.15638.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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Source: cited_works
Reference resolution
52 of 52 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 58b33c47-5ccd-4ce8-aa9b-66d379eb95b4 · outbound
This paper cites A survey of recent advances in particle filters and remaining challenges for multitarget tracking.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks A survey of recent advances in particle filters and remaining challenges for multitarget tracking
Reference 1
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Observation 93a9f190-22c1-4083-a124-f35efb117495 · outbound
This paper cites Particle learning for Bayesian semi-parametric stochastic volatility model.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Particle learning for Bayesian semi-parametric stochastic volatility model
Reference 2
- Resolution
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Observation 747c6145-eb27-4c58-82be-49aedb31b00d · outbound
This paper cites Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges
Reference 3
- Resolution
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Observation dd91088e-69e4-4e05-a123-5f73c2f5ee29 · outbound
This paper cites State-space models for ecological time-series data: Practical model-fi tting.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks State-space models for ecological time-series data: Practical model-fi tting
Reference 4
- Resolution
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Observation f20fc02b-dab2-4991-be18-baea5daeb75e · outbound
This paper cites Operat ional implementation of a hybrid ensemble /4d- Var global data assimilation system at the Met O ffice.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Operat ional implementation of a hybrid ensemble /4d- Var global data assimilation system at the Met O ffice
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation afc84777-65d0-4e0c-8789-523638815d79 · outbound
This paper cites Kalman and extended Kalman filters : Concept, derivation and properties.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Kalman and extended Kalman filters : Concept, derivation and properties
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation 61aad6d9-9ace-4e25-bd37-7596576097d2 · outbound
This paper cites The unscented Kalman filter for nonlinear estimation.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks The unscented Kalman filter for nonlinear estimation
Reference 7
- Resolution
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Source-reported events for the cited work
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Observation 9e16c362-1a32-449c-b14c-25e45b0a24e0 · outbound
This paper cites Novel approach to nonlinear and non- Gaussian Bayesian state estimation.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Novel approach to nonlinear and non- Gaussian Bayesian state estimation
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 5971850d-82ca-43d8-aa8d-f6383f97cbd7 · outbound
This paper cites Particle filtering.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Particle filtering
Reference 9
- Resolution
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Source-reported events for the cited work
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Observation 0ab90a89-5654-469c-b1ac-5adcc351ee2c · outbound
This paper cites A tutorial on part icle filtering and smoothing: Fifteen years later.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks A tutorial on part icle filtering and smoothing: Fifteen years later
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 204ff113-0360-4346-8045-679cf9991bca · outbound
This paper cites Cambridge University Press, 1st edition, 2013.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Cambridge University Press, 1st edition, 2013
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation b9f92a53-6c49-4b7a-9a9e-7f84804e89df · outbound
This paper cites Di fferentiable particle filtering via entropy-regularized optimal transport.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Di fferentiable particle filtering via entropy-regularized optimal transport
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation a1676c9a-cd4d-4271-a018-62bbe2eca548 · outbound
This paper cites Differentiable Particle Filtering without Modifying the Forward Pass.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Differentiable Particle Filtering without Modifying the Forward Pass
Reference 13
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Source-reported events for the cited work
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Observation 55693590-4111-48bf-8f76-8d087f9c5953 · outbound
This paper cites An overview of differentiable particle filters for data-adaptive sequential Bayesian inference.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
Reference 14
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Source-reported events for the cited work
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Observation 0ac5fe17-63df-4ddf-b5f8-aed508faf553 · outbound
This paper cites Differentiable Bootstrap Particle Filters for Regime-Switching Models.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Differentiable Bootstrap Particle Filters for Regime-Switching Models
Reference 15
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Source-reported events for the cited work
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Observation 62279bd0-48e1-4b80-88dd-186596bcec49 · outbound
This paper cites Particle filter networks with application to visual localization.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Particle filter networks with application to visual localization
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation 2cbf6115-3cf9-49ec-b19d-62ac69d4b158 · outbound
This paper cites Filtering via simulat ion: Auxiliary particle filters.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Filtering via simulat ion: Auxiliary particle filters
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation c45f036f-891c-460a-be70-7fa77d0b7bdc · outbound
This paper cites Elucidating the auxiliary particle filter via multiple importance sampling [lecture notes].
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Elucidating the auxiliary particle filter via multiple importance sampling [lecture notes]
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation ba0b42cc-209d-4410-81d1-09dd1b78202c · outbound
This paper cites Optimized auxili ary particle filters: adapting mixture proposals via convex optimization.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Optimized auxili ary particle filters: adapting mixture proposals via convex optimization
Reference 19
- Resolution
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Source-reported events for the cited work
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Observation 1ca1bbdc-a579-4109-bddd-359c2cec7694 · outbound
This paper cites Bugallo, and P etar M.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Bugallo, and P etar M
Reference 20
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Observation a062cba2-6fd6-42d2-8ceb-13ed6b3bebda · outbound
This paper cites Neural adaptive sequential Monte Carlo.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Neural adaptive sequential Monte Carlo
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation 7247b643-bb1c-495f-a465-415479c9262c · outbound
This paper cites V ariational sequential Monte Carlo.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks V ariational sequential Monte Carlo
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation 2819ff9a-c799-4acd-8e98-1c5c9d454b9e · outbound
This paper cites E nd- to-end learning of gaussian mixture proposals using di fferentiable particle filters and neural networks.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks E nd- to-end learning of gaussian mixture proposals using di fferentiable particle filters and neural networks
Reference 23
- Resolution
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Source-reported events for the cited work
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Observation fad418a6-f59a-4e40-be4c-3df9affa05e9 · outbound
This paper cites Improving population monte carlo: Alternative weighting and resampling schemes.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Improving population monte carlo: Alternative weighting and resampling schemes
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation 63550150-9191-47ec-b2d3-573891e81406 · outbound
This paper cites Unrolling particles: Unsupervise d learning of sampling distributions.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Unrolling particles: Unsupervise d learning of sampling distributions
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation fffa0938-0ed6-4c6e-96c0-3ae5a9e0826f · outbound
This paper cites Baraniuk, and Santiago Segarra.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Baraniuk, and Santiago Segarra
Reference 26
- Resolution
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Source-reported events for the cited work
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Observation fbdd72e5-cb9d-4790-baea-06d66807bdc4 · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Auto-Encoding Variational Bayes
Reference 27
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Observation 7690fc82-9e17-40c3-a862-f36e97e5c6b8 · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Towards Differentiable Resampling
Reference 28
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Observation 3ce7f240-abd0-4518-bce3-f548a804964c · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Adam: A Method for Stochastic Optimization
Reference 29
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Observation ab8f389a-b751-480d-901d-8f36cad52591 · outbound
This paper cites On the Variance of the Adaptive Learning Rate and Beyond.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks On the Variance of the Adaptive Learning Rate and Beyond
Reference 30
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Observation bdfaeb18-9935-4bbe-acf9-d50ea159ea7b · outbound
This paper cites Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks
Reference 31
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Source-reported events for the cited work
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Observation 86214eba-71a9-45fe-88f1-b13fa5aafc3f · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Jasper: An End-to-End Convolutional Neural Acoustic Model
Reference 32
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Observation 99b403d2-1323-4aa0-812d-bfe0818056b7 · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Adaptive importance sampling: The past, the present, and the future
Reference 33
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Observation 0a1d4060-0e8a-4018-afbd-3cd251199423 · outbound
This paper cites Recurrent neural networks: design and applications.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Recurrent neural networks: design and applications
Reference 34
- Resolution
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Observation 34172139-dcd2-4241-9ba4-367d7cc0a978 · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Bidirectional recu rrent neural networks
Reference 35
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Observation 3b30acc6-1d85-4b3b-b6ad-a113b5482a9d · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Recent Advances in Recurrent Neural Networks
Reference 36
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Source-reported events for the cited work
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This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasti ng.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Informer: Beyond efficient transformer for long sequence time-series forecasti ng
Reference 37
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Observation a3361c77-c5a5-4565-bacc-277dad94a195 · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Equinox: neural ne tworks in JAX via callable PyTrees and filtered transforma- tions
Reference 38
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Source-reported events for the cited work
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Observation e0f19a89-33b2-4700-955d-01c996e0c42f · outbound
This paper cites V ariational infere nce with normalizing flows.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks V ariational infere nce with normalizing flows
Reference 39
- Resolution
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Source-reported events for the cited work
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Observation 26b332a8-0c60-4fb2-bee4-dd711ccf0815 · outbound
This paper cites Conditional Density Estimation with Bayesian Normalising Flows.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Conditional Density Estimation with Bayesian Normalising Flows
Reference 40
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Observation 941fde58-8359-4fc2-8d9d-4181a869c846 · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Categorical Reparameterization with Gumbel-Softmax
Reference 41
- Resolution
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Source-reported events for the cited work
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Observation db5b18a7-05e3-43d8-bbb7-12b89f24b42f · outbound
This paper cites A family of nonpar ametric density estimation algorithms.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks A family of nonpar ametric density estimation algorithms
Reference 42
- Resolution
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Source-reported events for the cited work
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Observation 3e243cda-45db-476c-9493-641254c9eeec · outbound
This paper cites Pytorch: An imperative style, high-performance deep learning libra ry.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Pytorch: An imperative style, high-performance deep learning libra ry
Reference 43
- Resolution
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Source-reported events for the cited work
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Observation 172fe23e-c963-46e1-99db-fe9a3270cf66 · outbound
This paper cites JAX: composable transforma- tions of Python+NumPy programs, 2018.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks JAX: composable transforma- tions of Python+NumPy programs, 2018
Reference 44
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c8311bda-4b3c-44e1-ad6a-b9d7b5c1fd4e · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Unresolved cited work
Reference 45
- Resolution
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Source-reported events for the cited work
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Observation e9108f80-47a8-42a8-88fb-b3ebeda2f1ad · outbound
This paper cites Neural ordinary differential equations.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Neural ordinary differential equations
Reference 46
- Resolution
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Source-reported events for the cited work
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Observation eafb0f5e-2226-47cc-869f-f63e22bb592b · outbound
This paper cites On Robustness of Neural Ordinary Differential Equations.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks On Robustness of Neural Ordinary Differential Equations
Reference 47
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Source-reported events for the cited work
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Observation 708891e2-fed8-40ff-bf30-64409b071c13 · outbound
This paper cites DiffEqFlux.jl - A Julia Library for Neural Differential Equations.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks DiffEqFlux.jl - A Julia Library for Neural Differential Equations
Reference 48
- Resolution
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Source-reported events for the cited work
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Observation a2bfea7e-c07b-4f77-8f8b-caa1684ed380 · outbound
This paper cites Predictability: A problem partly solv ed.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Predictability: A problem partly solv ed
Reference 49
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6b2e51e8-98e4-482a-8c5a-e9aadda1bd2f · outbound
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Gaussian sum partic le filtering
Reference 50
- Resolution
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Source-reported events for the cited work
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Observation f56957e2-9054-4a89-a71d-749fed3f93dd · outbound
This paper cites Chemical Oscillations, W aves, and Turbulence.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Chemical Oscillations, W aves, and Turbulence
Reference 51
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- unresolved
- no resolver link, observed 2026-08-12T14:11:16.615572Z
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Observation f70c8e94-ae4f-4c7d-876a-7be28aae1571 · outbound
This paper cites an unresolved cited work.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Unresolved cited work
Reference 2023
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- malformed identifier
- no resolver link, observed 2026-08-12T14:11:16.538730Z
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Pith citing papers
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