Paper Citation Record · LEDGER

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images

As of 11 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2502.00754.

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2502.00754 v1

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measured 23 of 23 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-09T17:56:17.032786Z

measured 23 of 23 standing notices

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23 of 23 outbound references displayed

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Outbound references

Observation 861dd07b-5e44-46c7-b6f7-dc8d2c99d424 · outbound

This paper cites These values are intentionally larger than the theoretical minimum to enhance the model’s expressiveness.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images These values are intentionally larger than the theoretical minimum to enhance the model’s expressiveness

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.126690Z

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Observation 963cd602-7801-42a4-af8f-03c82efb2408 · outbound

This paper cites Hamiltonian neural networks.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Hamiltonian neural networks

Reference 4

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Observation a9b6cd75-2f22-4ddc-a408-3084f26dfec4 · outbound

This paper cites Kingma and Max Welling.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Kingma and Max Welling

Reference 6

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Observation 401fb6e6-a16d-489c-8182-bb64eaade200 · outbound

This paper cites Deep lagrangian networks: Using physics as model prior for deep learning.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Deep lagrangian networks: Using physics as model prior for deep learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.268061Z

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Observation 3dd95973-5374-43c3-9983-e33da04ddfde · outbound

This paper cites Resnet after all: Neural ODEs and their numerical solution.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Resnet after all: Neural ODEs and their numerical solution

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.257014Z

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Observation 7c982511-1b83-4048-8831-5755bd311920 · outbound

This paper cites We employ a very small VPNet for regularization.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images We employ a very small VPNet for regularization

Reference 12

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Observation 66e2b460-879b-4919-aa10-822b058328c3 · outbound

This paper cites Rezende, Andrew Jaegle, S ´ebastien Racani `ere, Aleksandar Botev, and Irina Higgins.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Rezende, Andrew Jaegle, S ´ebastien Racani `ere, Aleksandar Botev, and Irina Higgins

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.199341Z

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Observation 8597b081-386c-4163-a888-b1b0fc8ae959 · outbound

This paper cites Symmetry-Informed Governing Equation Discovery.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Symmetry-Informed Governing Equation Discovery

Reference 15

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Observation 2fbaa909-779b-4f2a-b07f-8c3a7382cc7b · outbound

This paper cites Symplectic ode-net: Learning hamiltonian dynamics with control.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Symplectic ode-net: Learning hamiltonian dynamics with control

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.187647Z

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Observation 3e4b5681-7f62-4ff0-bac3-d5938dd6d3fe · outbound

This paper cites an unresolved cited work.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:56:17.174679Z

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Observation 3c1df8c0-926c-4c49-9428-48b7a3b0af99 · outbound

This paper cites 15 Published as a conference paper at ICLR 2025 Given that maxl maxj1,j2 |ϕl(yl−1 1 , yl)|/(⌈Jl/2⌉δ) ≤ cϕ, we must choose Jl = O(1/δ).

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images 15 Published as a conference paper at ICLR 2025 Given that maxl maxj1,j2 |ϕl(yl−1 1 , yl)|/(⌈Jl/2⌉δ) ≤ cϕ, we must choose Jl = O(1/δ)

Reference 18

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Observation 464416e6-456a-48d0-844c-d48bfe9330b0 · outbound

This paper cites Due to weight sharing, the convolution operation exhibits translational invariance.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Due to weight sharing, the convolution operation exhibits translational invariance

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.149161Z

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Observation 64f56fda-e176-4438-8df6-26f7001f40d7 · outbound

This paper cites Finally, applying Lemma A.3, we have aL(∆) ≤ L−1X i=0 M i ϕMI KX k=1 cϕ 2l∗ k,1−1 |∆k,1| + cϕ 2l∗ k,2−1 |∆k,2|.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Finally, applying Lemma A.3, we have aL(∆) ≤ L−1X i=0 M i ϕMI KX k=1 cϕ 2l∗ k,1−1 |∆k,1| + cϕ 2l∗ k,2−1 |∆k,2|

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.137315Z

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Observation 7c4a2cd5-a688-4b71-a963-63fad35071f1 · outbound

This paper cites All convolutional layers are ac- companied by a batch normalization layer and a ReLU activation function.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images All convolutional layers are ac- companied by a batch normalization layer and a ReLU activation function

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.116263Z

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Observation c2a92ef0-901c-4cd5-a3f2-77ff8c981cdf · outbound

This paper cites Distilling free-form natural laws from experimental data.Science, 324(5923):81–85,.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Distilling free-form natural laws from experimental data.Science, 324(5923):81–85,

Reference 1985

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.223791Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cc6ed1c8-f9ae-4d7a-84fd-f7d2ded7e66e · outbound

This paper cites Auto-encoder based data clustering.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Auto-encoder based data clustering

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.212586Z

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Observation bf3d1978-16df-4e65-a6ef-d896820340ee · outbound

This paper cites A Brief Review of Hypernetworks in Deep Learning.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images A Brief Review of Hypernetworks in Deep Learning

Reference 2016

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Observation 4449f12f-46dd-4843-878c-db682f777c19 · outbound

This paper cites Unsupervised learning of invariant feature hierarchies with applications to object recognition.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Unsupervised learning of invariant feature hierarchies with applications to object recognition

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.235696Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0672f97-2769-42f1-b66b-34b9ec51bac5 · outbound

This paper cites Identification of dis- tributed parameter systems: A neural net based approach.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Identification of dis- tributed parameter systems: A neural net based approach

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.307597Z

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Observation be673a56-633c-40d5-9465-ff07f77a8b21 · outbound

This paper cites Variational autoencoder for deep learning of images, labels and captions.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Variational autoencoder for deep learning of images, labels and captions

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.245939Z

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Observation 5d2cba73-7a07-4f46-bc97-bfd8ec2ca828 · outbound

This paper cites Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

Reference 2022

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Observation 75106585-8f81-478b-99b8-9bff15e63828 · outbound

This paper cites Deep clustering with convolutional au- toencoders.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Deep clustering with convolutional au- toencoders

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:56:17.287923Z

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Observation 0f3e2f47-d0b7-4f44-aa96-a0a7ae82bd6d · outbound

This paper cites Lagrangian Neural Networks.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images Lagrangian Neural Networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T17:56:16.946521Z

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