Paper Citation Record · LEDGER

PyDPF: A Python Package for Differentiable Particle Filtering

As of 14 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2510.25693.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.25693 v3

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:31:49.171530Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T17:19:06.298574Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T17:24:57.607296Z

Reference resolution

56 of 56 outbound references displayed

verified exact2
verified fuzzy0
unresolved54
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41573834-34f3-4e0d-8868-f4f9eaa08034 · outbound

This paper cites Particle M arkov Chain M onte C arlo Methods.

PyDPF: A Python Package for Differentiable Particle Filtering Particle M arkov Chain M onte C arlo Methods

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.059134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb9471b2-0f8e-4554-80b5-fe7072cea2e0 · outbound

This paper cites DeepMind Lab.

PyDPF: A Python Package for Differentiable Particle Filtering DeepMind Lab

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.091452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2207a1d0-5687-419e-af6b-b243b2fdf54c · outbound

This paper cites Interacting Multiple Model Particle Filter.

PyDPF: A Python Package for Differentiable Particle Filtering Interacting Multiple Model Particle Filter

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.125072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 710121d8-467c-4cfd-abce-f6ec963b4bd0 · outbound

This paper cites JAX : Composable Transformations of Python + NumPy Programs.

PyDPF: A Python Package for Differentiable Particle Filtering JAX : Composable Transformations of Python + NumPy Programs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.158713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b255ab0-b528-49c0-b33e-d3fefc7a09e2 · outbound

This paper cites Differentiable Interacting Multiple Model Particle Filtering.

PyDPF: A Python Package for Differentiable Particle Filtering Differentiable Interacting Multiple Model Particle Filtering

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.199393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16a9a39c-7b79-462f-8304-774ed7ee309f · outbound

This paper cites LowLevelParticleFilters.jl.

PyDPF: A Python Package for Differentiable Particle Filtering LowLevelParticleFilters.jl

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.243887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b082ec13-4dd8-4636-95f1-08c1a6e13584 · outbound

This paper cites Improved Particle Filter for Nonlinear Problems.

PyDPF: A Python Package for Differentiable Particle Filtering Improved Particle Filter for Nonlinear Problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.279533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11fd7e0d-b9d1-4047-8434-82876f19714e · outbound

This paper cites Tracking Measles Infection through Non-Linear State Space Models.

PyDPF: A Python Package for Differentiable Particle Filtering Tracking Measles Infection through Non-Linear State Space Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.313791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a532d1db-375e-49fc-86c6-71e462cc923b · outbound

This paper cites Normalizing Flow-Based Differentiable Particle Filters.

PyDPF: A Python Package for Differentiable Particle Filtering Normalizing Flow-Based Differentiable Particle Filters

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:11.349168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0897ed4d-3ed1-4cbe-96ad-6ad6e0d46b10 · outbound

This paper cites An Introduction to Sequential M onte C arlo , chapter Particle Filtering, pp.

PyDPF: A Python Package for Differentiable Particle Filtering An Introduction to Sequential M onte C arlo , chapter Particle Filtering, pp

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:28.423733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bec5938-4792-4a4d-bf58-358fc8f995a1 · outbound

This paper cites Operational Implementation of a Hybrid Ensemble/4D- V ar Global Data Assimilation System at the M et O ffice.

PyDPF: A Python Package for Differentiable Particle Filtering Operational Implementation of a Hybrid Ensemble/4D- V ar Global Data Assimilation System at the M et O ffice

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:28.825223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 089c9a82-bd0a-4a7b-a9d1-99b1b3c3fa77 · outbound

This paper cites Differentiable Particle Filtering via Entropy-Regularized Optimal Transport.

PyDPF: A Python Package for Differentiable Particle Filtering Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:28.928969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c10335de-104e-4c0f-989d-971c72ff9bd0 · outbound

This paper cites End-to-end Learning of G aussian Mixture Proposals using Differentiable Particle Filters and Neural Networks.

PyDPF: A Python Package for Differentiable Particle Filtering End-to-end Learning of G aussian Mixture Proposals using Differentiable Particle Filters and Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.169880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e55eb6de-9285-4e38-b5a7-a96321b2c3e0 · outbound

This paper cites Sinkhorn Distances: Lightspeed Computation of Optimal Transport.

PyDPF: A Python Package for Differentiable Particle Filtering Sinkhorn Distances: Lightspeed Computation of Optimal Transport

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.345425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a84d1ec9-57cf-4953-8f0c-65e7d9d06ac1 · outbound

This paper cites an unresolved cited work.

PyDPF: A Python Package for Differentiable Particle Filtering Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.427474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef1574fc-0819-4441-ade9-3e24cd3e4350 · outbound

This paper cites Elucidating the Auxiliary Particle Filter via Multiple Importance Sampling.

PyDPF: A Python Package for Differentiable Particle Filtering Elucidating the Auxiliary Particle Filter via Multiple Importance Sampling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.518521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1807d170-d4c7-45a2-8ab4-458de32d2752 · outbound

This paper cites an unresolved cited work.

PyDPF: A Python Package for Differentiable Particle Filtering Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.578981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddda33a1-bc73-4f77-a986-8c9a64269ecf · outbound

This paper cites Turing : a Language for Flexible Probabilistic Inference.

PyDPF: A Python Package for Differentiable Particle Filtering Turing : a Language for Flexible Probabilistic Inference

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.674161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb33834a-0a20-488f-badb-4a161ad4ea40 · outbound

This paper cites Novel Approach to Nonlinear and Non- G aussian B ayesian State Estimation.

PyDPF: A Python Package for Differentiable Particle Filtering Novel Approach to Nonlinear and Non- G aussian B ayesian State Estimation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.782214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64322f7c-a281-4df7-b01c-16aad29a3f27 · outbound

This paper cites an unresolved cited work.

PyDPF: A Python Package for Differentiable Particle Filtering Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:29.897584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d349ff09-bf6b-4c9b-8922-2f37d253a1c3 · outbound

This paper cites Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors.

PyDPF: A Python Package for Differentiable Particle Filtering Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.003052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f30bd504-287c-4f0a-884a-bae7143b3fa5 · outbound

This paper cites A New Approach to Linear Filtering and Prediction Problems.

PyDPF: A Python Package for Differentiable Particle Filtering A New Approach to Linear Filtering and Prediction Problems

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.096151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34ccafc5-73c1-4992-98f4-a7fd54797c2a · outbound

This paper cites On Particle Methods for Parameter Estimation in State-Space Models.

PyDPF: A Python Package for Differentiable Particle Filtering On Particle Methods for Parameter Estimation in State-Space Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.198146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ed35507-8a12-488b-b3c9-6d4663ee5508 · outbound

This paper cites Particle Filter Networks with Application to Visual Localization.

PyDPF: A Python Package for Differentiable Particle Filtering Particle Filter Networks with Application to Visual Localization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.335555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0afde982-c7d4-41e1-9582-564d3239ae3f · outbound

This paper cites pomp : Statistical Inference for Partially Observed M arkov Processes.

PyDPF: A Python Package for Differentiable Particle Filtering pomp : Statistical Inference for Partially Observed M arkov Processes

Reference 25

Resolution: verified exact; doi, observed 2026-08-04T07:34:13.024777Z

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 87f65098-6e31-4db6-b68c-eb4ca2fc4fe3 · outbound

This paper cites Statistical Inference for Partially Observed M arkov Processes via the R Package pomp.

PyDPF: A Python Package for Differentiable Particle Filtering Statistical Inference for Partially Observed M arkov Processes via the R Package pomp

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.594466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c730b5-0b7a-4211-b2e2-376d58963293 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

PyDPF: A Python Package for Differentiable Particle Filtering Adam: A Method for Stochastic Optimization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.750718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 447c1143-56d1-41cb-b691-bc508af2ea34 · outbound

This paper cites Auto-Encoding Variational Bayes.

PyDPF: A Python Package for Differentiable Particle Filtering Auto-Encoding Variational Bayes

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.862806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 884db542-d253-400b-8df9-9b29804c7f22 · outbound

This paper cites Toward Practical N^2 M onte C arlo: the Marginal Particle Filter.

PyDPF: A Python Package for Differentiable Particle Filtering Toward Practical N^2 M onte C arlo: the Marginal Particle Filter

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:30.981537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c371922-c65e-447c-81ab-22eee3298b01 · outbound

This paper cites Auto-Encoding Sequential M onte C arlo.

PyDPF: A Python Package for Differentiable Particle Filtering Auto-Encoding Sequential M onte C arlo

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.105966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 239f4121-1e6c-4e90-9280-15759ab176f1 · outbound

This paper cites An Analysis of Regularized Interacting Particle Methods for Nonlinear Filtering.

PyDPF: A Python Package for Differentiable Particle Filtering An Analysis of Regularized Interacting Particle Methods for Nonlinear Filtering

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.215034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 433720d8-57ca-42d5-a297-0ea4c50d5c62 · outbound

This paper cites Revisiting Semi-Supervised Training Objectives for Differentiable Particle Filters.

PyDPF: A Python Package for Differentiable Particle Filtering Revisiting Semi-Supervised Training Objectives for Differentiable Particle Filters

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.277132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69b0017d-acc9-48f2-befe-21ab362e2366 · outbound

This paper cites Particle Gibbs with Ancestor Sampling.

PyDPF: A Python Package for Differentiable Particle Filtering Particle Gibbs with Ancestor Sampling

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.357138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ae55655-fb29-47ae-9ab8-f5423b359da2 · outbound

This paper cites an unresolved cited work.

PyDPF: A Python Package for Differentiable Particle Filtering Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.425925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76c38405-e92a-484e-b05d-e9d371d1ddc4 · outbound

This paper cites MATLAB Control System Toolbox.

PyDPF: A Python Package for Differentiable Particle Filtering MATLAB Control System Toolbox

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.556449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3221c4dd-40c2-4b42-a899-eb1eb1f2b008 · outbound

This paper cites M onte C arlo Gradient Estimation in Machine Learning.

PyDPF: A Python Package for Differentiable Particle Filtering M onte C arlo Gradient Estimation in Machine Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.757430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ac00b86-6ded-4a43-be7a-b1a10df057a8 · outbound

This paper cites Feynman- K ac Formulae: Genealogical and Interacting Particle Systems with Applications.

PyDPF: A Python Package for Differentiable Particle Filtering Feynman- K ac Formulae: Genealogical and Interacting Particle Systems with Applications

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:31.906400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 905230b6-cbd4-4a54-bfd4-d00abf1fa26d · outbound

This paper cites pypfilt : a Particle Filter for Python.

PyDPF: A Python Package for Differentiable Particle Filtering pypfilt : a Particle Filter for Python

Reference 38

Resolution: verified exact; doi, observed 2026-08-04T07:34:12.870530Z

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 1f367321-e936-4885-bf90-988e3ff4034e · outbound

This paper cites Improving Regularized Particle Filters.

PyDPF: A Python Package for Differentiable Particle Filtering Improving Regularized Particle Filters

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:32.247408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c1d4863-8f4d-4f21-b12f-05a7a51ab305 · outbound

This paper cites Variational Sequential M onte C arlo.

PyDPF: A Python Package for Differentiable Particle Filtering Variational Sequential M onte C arlo

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.021622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c55c0f31-1a01-4bb3-aa4f-2fc590679115 · outbound

This paper cites an unresolved cited work.

PyDPF: A Python Package for Differentiable Particle Filtering Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.036863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e80aab3b-faec-4924-ab5e-640bbfb40f5b · outbound

This paper cites A Simplex Method for Function Minimization.

PyDPF: A Python Package for Differentiable Particle Filtering A Simplex Method for Function Minimization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.046698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5ca972e-3225-42ff-8432-5435e338d416 · outbound

This paper cites State-Space Models for Ecological Time-Series Data: Practical Model-Fitting.

PyDPF: A Python Package for Differentiable Particle Filtering State-Space Models for Ecological Time-Series Data: Practical Model-Fitting

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.054746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 572006ad-27a0-4fc1-896f-fece17b77934 · outbound

This paper cites Variational Bayesian inference with stochastic search.

PyDPF: A Python Package for Differentiable Particle Filtering Variational Bayesian inference with stochastic search

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.064598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d04317af-0a47-48c5-b8ea-5d7d2ed26968 · outbound

This paper cites Normalizing Flows for Probabilistic Modeling and Inference.

PyDPF: A Python Package for Differentiable Particle Filtering Normalizing Flows for Probabilistic Modeling and Inference

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.075044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29e65b32-bfa1-474f-8198-8ed97604e936 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

PyDPF: A Python Package for Differentiable Particle Filtering PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.084731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e75b592-2d81-4065-8c38-b598a03b7191 · outbound

This paper cites Filtering via Simulation: Auxiliary Particle Filters.

PyDPF: A Python Package for Differentiable Particle Filtering Filtering via Simulation: Auxiliary Particle Filters

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.094667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bf33a3d-9559-4ffe-ac58-000b3bc0c76f · outbound

This paper cites Bayesian Filtering and Smoothing, volume 17.

PyDPF: A Python Package for Differentiable Particle Filtering Bayesian Filtering and Smoothing, volume 17

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.102441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea4fa2a0-9648-45b1-946b-e896b0a3838b · outbound

This paper cites Differentiable Particle Filtering without Modifying the Forward Pass.

PyDPF: A Python Package for Differentiable Particle Filtering Differentiable Particle Filtering without Modifying the Forward Pass

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.112382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9ba8b47-04ea-4515-887f-8f6be733f65a · outbound

This paper cites Particle Learning for B ayesian Semi-Parametric Stochastic Volatility Model.

PyDPF: A Python Package for Differentiable Particle Filtering Particle Learning for B ayesian Semi-Parametric Stochastic Volatility Model

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.123387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 820a4631-7c73-40cc-8028-dc60f821f9b8 · outbound

This paper cites A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking.

PyDPF: A Python Package for Differentiable Particle Filtering A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.131237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8bdd7e4-7cf6-4741-b188-b1c5534a0221 · outbound

This paper cites Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.

PyDPF: A Python Package for Differentiable Particle Filtering Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.137786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 210bc30f-2c4b-4631-a8a7-ff694e8e9eef · outbound

This paper cites Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes.

PyDPF: A Python Package for Differentiable Particle Filtering Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.147904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b50f26ad-2279-4bbc-a5b3-d880876059e4 · outbound

This paper cites Learning to be Smooth: An End-to-End Differentiable Particle Smoother.

PyDPF: A Python Package for Differentiable Particle Filtering Learning to be Smooth: An End-to-End Differentiable Particle Smoother

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.155560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9b09d31-bf24-4469-9c36-046869a90ff3 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

PyDPF: A Python Package for Differentiable Particle Filtering , " * write output.state after.block = add.period write newline

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.162939Z

Source-reported events for the cited work

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Observation 80ed7fc6-efa3-474e-9e6e-961e58b023ed · outbound

This paper cites write newline.

PyDPF: A Python Package for Differentiable Particle Filtering write newline

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.171530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6cba426e-357b-41b3-a611-8484ca9bbb77 · inbound

Efficient Learning of Deep State Space Models via Importance Smoothing cites this paper.

Efficient Learning of Deep State Space Models via Importance Smoothing PyDPF: A Python Package for Differentiable Particle Filtering

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-08-04T02:39:44.154761Z

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 4c7b10e3-639f-44f0-b911-2a40d12ac25f · inbound

Efficient Learning of Deep State Space Models via Importance Smoothing cites this paper.

Efficient Learning of Deep State Space Models via Importance Smoothing PyDPF: A Python Package for Differentiable Particle Filtering

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-08-04T02:39:44.154761Z

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.