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Paper Citation Record · LEDGER

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations

As of 11 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2501.01339.

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

pith.paper-citation-record.v1
2501.01339 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

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measured 17 of 17 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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

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Reference resolution

17 of 17 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3124af41-5638-4321-b8ab-62a858927b56 · outbound

This paper cites A Non-Linear Kalman Filter for track parameters estimation in High Energy Physics.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations A Non-Linear Kalman Filter for track parameters estimation in High Energy Physics

Reference 1

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Observation 1e88d5a8-6813-4df1-ae36-71c2bd199537 · outbound

This paper cites Stability of discrete dynamical systems.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Stability of discrete dynamical systems

Reference 2

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Observation 731e1a2b-4c3e-4538-8509-a1c657b68e20 · outbound

This paper cites OpenAI Gym.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations OpenAI Gym

Reference 3

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Observation 94737f02-0fbb-42a5-aaec-f5cfe2c93ffb · outbound

This paper cites Discover- ing governing equations from data by sparse identification of nonlinear dynamical systems.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Discover- ing governing equations from data by sparse identification of nonlinear dynamical systems

Reference 4

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Observation 9e02a735-6ae8-4a8b-9fda-bbc0682ae1eb · outbound

This paper cites Data-driven discovery of coordinates and governing equations.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Data-driven discovery of coordinates and governing equations

Reference 5

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Observation e8d3181a-264f-458d-87fb-f0cf9df189d8 · outbound

This paper cites Differentiable Particle Filters through Conditional Normalizing Flow.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Differentiable Particle Filters through Conditional Normalizing Flow

Reference 6

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Observation 86e52274-103c-4fbd-b1f1-7be893641c21 · outbound

This paper cites Normalizing Flows Across Dimensions.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Normalizing Flows Across Dimensions

Reference 7

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Observation decb4ad4-ad16-4da4-a8e8-c92e74777543 · outbound

This paper cites Dorf and Robert H.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Dorf and Robert H

Reference 8

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Observation fa243195-ceec-4841-9156-073623a67b63 · outbound

This paper cites Optimal Gene Filtering for Single-Cell data (OGFSC)—a gene filtering algorithm for single-cell RNA-seq data.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Optimal Gene Filtering for Single-Cell data (OGFSC)—a gene filtering algorithm for single-cell RNA-seq data

Reference 9

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Observation 0720ce1c-cd5f-4067-be06-75d06e879f04 · outbound

This paper cites A New Approach to Linear Filtering and Prediction Prob- lems.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations A New Approach to Linear Filtering and Prediction Prob- lems

Reference 10

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Observation d2a06988-d698-4f84-a078-c3a542fc8551 · outbound

This paper cites Funnels: Exact maximum likelihood with dimensionality reduction.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Funnels: Exact maximum likelihood with dimensionality reduction

Reference 11

Resolution
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Observation 98027f77-509f-47af-a542-369027ad2a81 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Playing Atari with Deep Reinforcement Learning

Reference 12

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Source-reported events for the cited work

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Observation 9482cb42-f6e2-4eae-8bad-258f597a4070 · outbound

This paper cites Normalizing Flows for Probabilistic Modeling and Inference.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Normalizing Flows for Probabilistic Modeling and Inference

Reference 13

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Observation 79703b29-c2a4-4751-8180-f818c033a6ca · outbound

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

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 14

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Observation b52a78b2-e426-4c6d-89fa-392d20b972f6 · outbound

This paper cites Cambridge University Press, 2013.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Cambridge University Press, 2013

Reference 15

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Observation 02e13657-81f2-4aa3-bb83-3a129057648a · outbound

This paper cites Learning Likelihoods with Conditional Normalizing Flows.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Learning Likelihoods with Conditional Normalizing Flows

Reference 16

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Observation 3c15f94a-e39d-4bea-95e1-07fa98ea218a · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations Playing Atari with Deep Reinforcement Learning

Reference 2013

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