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Paper Citation Record · LEDGER

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics

As of 20 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 0 inbound Pith citation observations for arXiv:2505.18107.

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

pith.paper-citation-record.v1
2505.18107 v1

Coverage vector

measured 94 of 94 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:40:27.654568Z

measured 94 of 94 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

94 of 94 outbound references displayed

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External citation measurements

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Outbound references

Observation cfd69840-9bf9-4f01-91b2-86dded7f5e64 · outbound

This paper cites Intrinsic dimensionality explains the effectiveness of language model fine-tuning.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Intrinsic dimensionality explains the effectiveness of language model fine-tuning

Reference 1

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Observation 6ac0ac8e-6f73-4aca-b87d-d70ad81ca0cb · outbound

This paper cites Towards efficient image compression without autoregressive models.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Towards efficient image compression without autoregressive models

Reference 2

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Observation 1a55d1bb-6572-40d3-b858-ebfdda7a773d · outbound

This paper cites Nonlinear transform coding.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Nonlinear transform coding

Reference 3

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Observation e7f6eff6-f748-4910-bd60-318ff7c4b48e · outbound

This paper cites Variational image compression with a scale hyperprior.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Variational image compression with a scale hyperprior

Reference 4

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Observation 594793aa-a7e1-47d4-9132-93076e8d8cb0 · outbound

This paper cites Instereo2k: a large real dataset for stereo matching in indoor scenes.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Instereo2k: a large real dataset for stereo matching in indoor scenes

Reference 5

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Observation 7b781ee3-573d-4602-a620-64e3fd3d6bb3 · outbound

This paper cites Image reconstruction via deep image prior subspaces.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Image reconstruction via deep image prior subspaces

Reference 6

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Observation 14c07176-b1c1-42f6-be73-482e96cf794d · outbound

This paper cites Compressai: a pytorch library and evaluation platform for end-to-end compression research, 2020.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Compressai: a pytorch library and evaluation platform for end-to-end compression research, 2020

Reference 7

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Observation 8af27451-f0e8-41e7-96f0-ea462d466736 · outbound

This paper cites Bj ntegaard.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Bj ntegaard

Reference 8

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Observation 32dfc7c7-6dba-424d-ac82-29cdf026e88b · outbound

This paper cites Enhancing neural training via a correlated dynamics model.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Enhancing neural training via a correlated dynamics model

Reference 9

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Observation d9bab46d-31ff-46ed-a94a-a6c65129368b · outbound

This paper cites Brunton, Marko Budi s i\' c , Eurika Kaiser, and J.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Brunton, Marko Budi s i\' c , Eurika Kaiser, and J

Reference 10

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Observation e92086ec-b67f-4579-a5cf-a93e61b73691 · outbound

This paper cites Robust overfitting may be mitigated by properly learned smoothening.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Robust overfitting may be mitigated by properly learned smoothening

Reference 11

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Observation 113f5cd6-6872-4306-9584-90c1e2d1ff8c · outbound

This paper cites Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution

Reference 12

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Observation 89ae4ad7-4edf-47dd-94b0-2f751e434911 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics The cityscapes dataset for semantic urban scene understanding

Reference 13

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Observation 2eed98e3-3572-466e-9fc6-24577bf2ab98 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 14

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Observation 98f7bc4b-7d07-4e94-acbc-dc6a9a3ed72c · outbound

This paper cites Optimizing neural networks via koopman operator theory.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Optimizing neural networks via koopman operator theory

Reference 15

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Observation 438cb2c5-bc74-4595-b367-158d751e3a81 · outbound

This paper cites Qarv: Quantization-aware resnet vae for lossy image compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Qarv: Quantization-aware resnet vae for lossy image compression

Reference 16

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Observation 56198e04-e6a9-499c-9ec4-b646b6d41030 · outbound

This paper cites Asymmetric numeral systems, 2009.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Asymmetric numeral systems, 2009

Reference 17

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Observation 93d0068a-33c0-4400-a601-ed56cdac8bad · outbound

This paper cites Rigging the lottery: Making all tickets winners.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Rigging the lottery: Making all tickets winners

Reference 18

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Observation 5a77d9fc-d7bb-471b-95fa-01e6d2389673 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Understanding the difficulty of training deep feedforward neural networks

Reference 19

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Observation ae397d78-0cc9-473e-8e28-9daab972184c · outbound

This paper cites Improving neural network training in low dimensional random bases.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Improving neural network training in low dimensional random bases

Reference 20

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Observation 316b17fa-397e-428a-a9fc-ab87a4a7456d · outbound

This paper cites EVC : Towards real-time neural image compression with mask decay.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics EVC : Towards real-time neural image compression with mask decay

Reference 21

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Observation e334720b-c5e1-4221-b48c-1beae939edd9 · outbound

This paper cites Checkerboard context model for efficient learned image compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Checkerboard context model for efficient learned image compression

Reference 22

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Observation 36189e10-667e-4124-b29d-07b16dd5b363 · outbound

This paper cites E LIC : Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics E LIC : Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding

Reference 23

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Observation f53ea00c-2223-473a-b1df-e8a18a057171 · outbound

This paper cites Asymmetric valleys: Beyond sharp and flat local minima.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Asymmetric valleys: Beyond sharp and flat local minima

Reference 24

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Observation 620add2b-39ad-4fb5-a8c3-d87a14de07aa · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 25

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Observation 7cfcdf48-881a-4b49-9403-3331bfeedf68 · outbound

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Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Momentum contrast for unsupervised visual representation learning

Reference 26

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Observation 66e40b20-cfa7-49ca-848b-925e0c0d76fd · outbound

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Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Lo RA : Low-rank adaptation of large language models

Reference 27

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Accelerating Learned Image Compression Through Modeling Neural Training Dynamics JPEG AI Common Training & Test Conditions v8.0

Reference 28

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Observation 2c179f03-4e6d-410e-86d5-69619343637f · outbound

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Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Garipov, Dmitry P

Reference 29

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Observation 722f8335-05c5-488b-8fc7-b403a769db1f · outbound

This paper cites Visual prompt tuning.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Visual prompt tuning

Reference 30

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This paper cites Towards Practical Real-Time Neural Video Compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Towards Practical Real-Time Neural Video Compression

Reference 31

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This paper cites Variable-rate learned image compression with multi-objective optimization and quantization-reconstruction offsets.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Variable-rate learned image compression with multi-objective optimization and quantization-reconstruction offsets

Reference 32

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Observation e9379a74-c636-4272-8eb6-c6b301872862 · outbound

This paper cites Multi-layer random perturbation training for improving model generalization efficiently.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Multi-layer random perturbation training for improving model generalization efficiently

Reference 33

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This paper cites A software platform for manipulating the camera imaging pipeline.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics A software platform for manipulating the camera imaging pipeline

Reference 34

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Observation 59c40a22-9f44-46d5-92bd-9b494768fd2f · outbound

This paper cites Understanding black-box predictions via influence functions.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Understanding black-box predictions via influence functions

Reference 35

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Observation 49ae1eaf-8fe4-4231-88a0-0c2995cd310b · outbound

This paper cites Dynamic sparse training with structured sparsity.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Dynamic sparse training with structured sparsity

Reference 36

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Observation f553e8cd-4157-4585-8886-173f6d730080 · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Measuring the intrinsic dimension of objective landscapes

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.999151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:22.366149Z digest=sha256:ce92e2122f3608d41148d4e2e9cc4552c4e141495fc170d51f8342ee3f1c5711

Observation 1cb84058-ead6-464c-be82-e08e126b33a8 · outbound

This paper cites Frequency-aware transformer for learned image compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Frequency-aware transformer for learned image compression

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.859917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:22.483827Z digest=sha256:e8e8cddbd81eaaf0813aa609de074ecf957ac11154dc914239652077f71a9df6

Observation d8856e3c-71c0-4c98-b7b3-de0808c1b698 · outbound

This paper cites Deep contextual video compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Deep contextual video compression

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.740087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:22.568531Z digest=sha256:2500ef93a6fce1da77a9cc8b266205bc0babe7e94bc48a456a773ed37f970b4f

Observation 8853efff-f552-4d32-8ca6-09fb1eeed74e · outbound

This paper cites Hybrid spatial-temporal entropy modelling for neural video compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Hybrid spatial-temporal entropy modelling for neural video compression

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.639781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:22.675889Z digest=sha256:efc8bd5d34a83edf036fff58c1dbd4222b7576a2f193d6428cdb85581b1e3062

Observation 98065066-0fbb-42ec-b803-c9d14258f758 · outbound

This paper cites Neural video compression with diverse contexts.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Neural video compression with diverse contexts

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.532421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:22.762142Z digest=sha256:9e4ca6983b9d62b0277e88e2b1346a02a10ffd519e816c790743b6c5ac695c2a

Observation b7c5e1f9-48a5-4cc8-ae56-8022f5eb0f3b · outbound

This paper cites Neural video compression with feature modulation.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Neural video compression with feature modulation

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.407679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:22.845851Z digest=sha256:e18bb2ee1d988f9ff7faedd6c7dc446207a5f7eb504bcfab9beaf4ab7afbb5c2

Observation 571f13a1-553c-4ae3-a9d3-d65ed6d9c68a · outbound

This paper cites Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.277187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:22.948924Z digest=sha256:3280cd8e11a5f967084153ee42b10e17314aeaafe1ea242484f80bef874e14ea

Observation 78e8e90f-9742-406c-8bb9-afae4eff7d60 · outbound

This paper cites Subspace adversarial training.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Subspace adversarial training

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.160780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:23.071548Z digest=sha256:4b1c643c40362093280bfd0682ec33bc0036b70bd8384f6dd9ce731803136034

Observation 9e41822b-38d6-40a4-98da-aecce228320c · outbound

This paper cites Trainable weight averaging: Efficient training by optimizing historical solutions.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Trainable weight averaging: Efficient training by optimizing historical solutions

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:34.041947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:23.171024Z digest=sha256:1793f26f984305972e9896088ae18a1556a8b640235d1f51ba5f977a4f807a0b

Observation 872dd063-6759-4b7b-8319-fdd6b68cbdde · outbound

This paper cites Revisiting random weight perturbation for efficiently improving generalization.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Revisiting random weight perturbation for efficiently improving generalization

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:33.861347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:23.268800Z digest=sha256:46791fdbb7a347b5446d9e7a64d0d1bae2753998913935c3b9a394090bbbc877

Observation 5660d6f3-d515-446c-82f2-b74260099266 · outbound

This paper cites Deep model fusion: A survey, 2023 c.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Deep model fusion: A survey, 2023 c

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:33.675318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:23.411174Z digest=sha256:866e245d45505ade5418a8769409a40de96939e39d2ee65897df48b69b5df637

Observation e572d4c3-9408-46d1-aab6-8e71c30c4466 · outbound

This paper cites Microsoft COCO : Common O bjects in C ontext.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Microsoft COCO : Common O bjects in C ontext

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:33.481236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:23.514786Z digest=sha256:afe3f3201d1ba27f94615513e047714da9e4ec80a1590cfdd96f86264ed1612d

Observation f2935fc9-5b46-447c-bf67-d0bdaa8e220e · outbound

This paper cites Learned image compression with mixed transformer-cnn architectures.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Learned image compression with mixed transformer-cnn architectures

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:33.302196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:23.632502Z digest=sha256:08d3d76e4d11f565d0ce9447a328862eae57e009c04a8e5972d18fc0ede5d6ed

Observation 19b1ab4e-9b1f-4f99-a19f-51e405d575e2 · outbound

This paper cites Bidirectional stereo image compression with cross-dimensional entropy model.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Bidirectional stereo image compression with cross-dimensional entropy model

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:33.147312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:23.709971Z digest=sha256:e5829750d4f51823257c0397635c47e41717991cf9ebdff4e2aa9d5f5d779f8b

Observation ed019871-7690-40b9-b49b-eeda681d2e86 · outbound

This paper cites Deep learning for universal linear embeddings of nonlinear dynamics.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Deep learning for universal linear embeddings of nonlinear dynamics

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:23.825254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:40:23.825254Z digest=sha256:d1c91e9c35b2073bd1125b6acf507a156b41c7727e80adff53cfead4031f7e97

Observation c494008d-afee-4ff5-99e2-a4c48d6a778c · outbound

This paper cites New insights and perspectives on the natural gradient method.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics New insights and perspectives on the natural gradient method

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:23.930934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:40:23.930934Z digest=sha256:3116541c32c364cee37ccc610bb078a7ac7f6e78adc8508053e17bf11361199c

Observation e42376b4-328c-4c72-8c8c-53a64d16e2da · outbound

This paper cites Advancing the rate-distortion-computation frontier for neural image compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Advancing the rate-distortion-computation frontier for neural image compression

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.993180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.019837Z digest=sha256:f2ba13c7ba621baefe5bdbba243190ac7466f8880f377d0b2430866bdb3da288

Observation 1c308536-1b8a-46f0-a9fd-9411863a19d7 · outbound

This paper cites Channel-wise autoregressive entropy models for learned image compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Channel-wise autoregressive entropy models for learned image compression

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.855079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.126857Z digest=sha256:e4317f4db94c0ffc6c35ba3223fdca28df706c0704af84e5d16b243216da6941

Observation 787bfb5a-5c26-4369-9905-d6acaf2ea8e1 · outbound

This paper cites Importance estimation for neural network pruning.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Importance estimation for neural network pruning

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.682265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.224873Z digest=sha256:05c83ce40ce3fd3705c6573a3d8569280766d84cc61f7d12d0076b06aa2a9514

Observation 08fef050-7eee-4ecf-ae43-caf3f5e3399e · outbound

This paper cites Exponential moving average of weights in deep learning: Dynamics and benefits.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Exponential moving average of weights in deep learning: Dynamics and benefits

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.601547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.341155Z digest=sha256:89a1d535842377a09990ad143484e15f75e6d48c17a74989dafa62ad72e87ac0

Observation ee599064-b62d-4ae5-bf11-140ab2ee1ffa · outbound

This paper cites Decomposed linear dynamical systems (dlds) for learning the latent components of neural dynamics.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Decomposed linear dynamical systems (dlds) for learning the latent components of neural dynamics

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.491341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.449683Z digest=sha256:8acc555b08d14cd239043878e2b72b42ff86d681b6cee302c2c07b52a901fbb1

Observation 8cdadf09-49f7-4faa-8184-97d3ef9a6766 · outbound

This paper cites Modern hierarchical, agglomerative clustering algorithms, 2011.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Modern hierarchical, agglomerative clustering algorithms, 2011

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.379893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.577057Z digest=sha256:3d6893fcefda618cb69e0cb419641dfd1e1cf9afee5e869a33ee0698f150ab8f

Observation 4dd99bd2-deb6-478e-91b7-c55b36da252b · outbound

This paper cites Learning srgb-to-raw-rgb de-rendering with content-aware metadata.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Learning srgb-to-raw-rgb de-rendering with content-aware metadata

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.104482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.660371Z digest=sha256:95e56e48d84031e74bb48796570a37e7b17c9c086ad6b75e39537fc10d45d537

Observation 0bf1b320-575f-4514-9ba7-6dcbf928c714 · outbound

This paper cites A review on weight initialization strategies for neural networks.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics A review on weight initialization strategies for neural networks

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:32.034290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.764494Z digest=sha256:72861d50db34879b83f0d06aaca037d308b70f1b44386435a1d9525907199568

Observation 2b662ab0-f01f-4b99-bae2-fec4b31d630f · outbound

This paper cites Scid: A database for screen content images quality assessment.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Scid: A database for screen content images quality assessment

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:31.920894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.857388Z digest=sha256:793b5fe6b97054222d9c44f351a996b8f803d56e0baa61f0277a3bbf93a156f1

Observation 74465b39-c727-4c93-a6bd-d31e4f9eed0d · outbound

This paper cites Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:31.775093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:24.966457Z digest=sha256:b41797b73b2669eb92ab9c185dcd0de64d812b534e1fc5de3f8d41f95d4ff660

Observation cf149ebf-e462-4be2-b33f-e7686cbb61f2 · outbound

This paper cites Banach's fixed point theorem for partial metric spaces, 2004.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Banach's fixed point theorem for partial metric spaces, 2004

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:31.677039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.083010Z digest=sha256:5f9f234483a1c3bb13c631dd6fcf36e98acbf5e45e6755e01672c3a80c81bb92

Observation d10347c9-ba89-48e5-abae-2a951b197c45 · outbound

This paper cites Acceleration of stochastic approximation by averaging.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Acceleration of stochastic approximation by averaging

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:25.175436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:40:25.175436Z digest=sha256:17f9a8e393f56292a428474dc6d22aacd124ec846d351f5d90a45e51adbc43ee

Observation 16d610e2-a9df-46ad-9d0d-fbe3e88d3a2d · outbound

This paper cites Your transformer is secretly linear, 2024.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Your transformer is secretly linear, 2024

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:31.556509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.263332Z digest=sha256:4ba138c0d89999aaa1c056ccec9206a4639863dc4ebc053018961da0bc3305d7

Observation 54f9f8d7-6819-4824-aae1-ff8a96cab369 · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, 2013.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, 2013

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:31.419023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.365570Z digest=sha256:c12f28b7dc1fc4e8b3820a23ec7c394a2dd7bacad14f9ed4bdd7c66650e29d4a

Observation dd1c9917-7f12-48fd-8792-f73d58dac923 · outbound

This paper cites No more pesky learning rates.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics No more pesky learning rates

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:31.287171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.483866Z digest=sha256:1a926fdb2418b83c8e00b3f27c03b299f045002978d607e2109fdac34caef6a2

Observation 89c54123-1894-4653-a6ba-028d12ccc12a · outbound

This paper cites Dynamic mode decomposition of numerical and experimental data.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Dynamic mode decomposition of numerical and experimental data

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:31.038086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.600168Z digest=sha256:0b6f5aa39b6dc61cf0d697bd8c3b2e39b194395b6a41ae5465c8561ba14d9dab

Observation 15bddbe3-ca0d-4413-9b5a-2e1534486706 · outbound

This paper cites Dynamic mode decomposition and its variants.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Dynamic mode decomposition and its variants

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:30.844742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.682208Z digest=sha256:cd53f8806c24cc41bb83a8b428ead719253b0e7c065c24c60d419665bc1e7d87

Observation 101c1d58-e2f5-47e2-af92-28794b88d6fd · outbound

This paper cites Temporal context mining for learned video compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Temporal context mining for learned video compression

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:30.598244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.804457Z digest=sha256:7a3fad76818c0fb6cc5e75be62d39549e836a106118f41fcaebd9c3e01277f1f

Observation 4bc4adb3-6b21-4dad-b9b8-4f853f4a7c42 · outbound

This paper cites Consistency models.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Consistency models

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:30.516547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:25.931294Z digest=sha256:830fd381fae3abbffd3cfe31b69a6e83d877f40eee1400b16c8c673760036dd5

Observation 59b364a1-1d47-4c89-9218-0700c76874d3 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Rethinking the inception architecture for computer vision

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:30.404984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.026125Z digest=sha256:004fb7ef2fe471fda1552eb14d2266894a6ba9408fc10ac25a500c901e7dfb43

Observation 372e46a7-3498-4237-9dcc-80ffcd6b4c3c · outbound

This paper cites Adanic: Towards practical neural image compression via dynamic transform routing.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Adanic: Towards practical neural image compression via dynamic transform routing

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:30.219621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.129991Z digest=sha256:eb7ec64327ab875c1943c552d3fc5af576554e978709e1c56e0b87578336c612

Observation e8da9bc6-4037-4052-994f-4f11df0c27bd · outbound

This paper cites Beyond learned metadata-based raw image reconstruction.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Beyond learned metadata-based raw image reconstruction

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.996674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.267603Z digest=sha256:fbac8bb5f8cf170949366cd52b82fa0be6710c27891148a44824684b94377f81

Observation bfbaf1bf-0e3a-4d58-9c93-c0851c5d8ef4 · outbound

This paper cites Towards certificated model robustness against weight perturbations.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Towards certificated model robustness against weight perturbations

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.780903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.377015Z digest=sha256:a0961d74c365050fa21278de2dc4b0ac6100026d5623ee3f1bd081b67275d7e2

Observation 2b512526-18e2-4977-a210-d1dc8281fcf4 · outbound

This paper cites Ecsic: Epipolar cross attention for stereo image compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Ecsic: Epipolar cross attention for stereo image compression

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.632323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.472757Z digest=sha256:c9352a5bda4e689f448a66542247c6d13d1ecdc6e842a46176d8d001297e99ee

Observation 9abdaaab-f3f7-469d-a87e-037983bf82e4 · outbound

This paper cites Understanding short-horizon bias in stochastic meta-optimization.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Understanding short-horizon bias in stochastic meta-optimization

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.528666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.582899Z digest=sha256:d5f2cb2b00cb1977cdc158c25c3891386ac007d3e684a119a9ae60b4fa74f57f

Observation 980910a2-edd5-4491-b82d-3c6385cd2a95 · outbound

This paper cites Remote sensing image compression based on high-frequency and low-frequency components.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Remote sensing image compression based on high-frequency and low-frequency components

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.420587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.666916Z digest=sha256:c031203958c0a0e675e0e3985740ecd7330ace46f9e815064a0322574738912c

Observation 1dbd9c4f-b3b8-4ae4-8048-3c7007c819c9 · outbound

This paper cites Perceptual quality assessment of screen content images.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Perceptual quality assessment of screen content images

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.335435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.714062Z digest=sha256:b975dc1e7b5026529bd4d6e5aac231b69447315d2376f1cbaddf82c45d485b93

Observation 0a71cccf-cd7c-4d0d-aac3-687b3d260ba3 · outbound

This paper cites Deep neural network pruning method based on sensitive layers and reinforcement learning.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Deep neural network pruning method based on sensitive layers and reinforcement learning

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.238099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.759741Z digest=sha256:239b30608dde64f5c50cce0f40b694a48677fb523eab287ad239a56e3e4c8c19

Observation fe6998ff-fa40-465a-b194-f6fb6393cc65 · outbound

This paper cites Computationally-efficient neural image compression with shallow decoders.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Computationally-efficient neural image compression with shallow decoders

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.120583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.804802Z digest=sha256:021a7585255bce7e13c7f2d03cfb763b9e3712fce39374392d54ba6751ca9056

Observation 15a68477-5c10-4e4a-bbc0-9da29c9cd711 · outbound

This paper cites Safer: Layer-level sensitivity assessment for efficient and robust neural network inference, 2023.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Safer: Layer-level sensitivity assessment for efficient and robust neural network inference, 2023

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:29.012804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:26.911471Z digest=sha256:07a25db17c34da570649eea0bb0fbd56942feed13867340fcd6bca137df9c995

Observation ba371aec-9068-475d-8adb-3ab2d4c61f29 · outbound

This paper cites Are all layers created equal? Journal of Machine Learning Research, 23 0 (67): 0 1--28, 2022.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Are all layers created equal? Journal of Machine Learning Research, 23 0 (67): 0 1--28, 2022

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.899412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.044391Z digest=sha256:e16fe13861d88803fa22dbe53da9b939f56353a378910c907727f9714997de29

Observation 7fd68a0a-2f06-410c-b221-897efcbfbb4f · outbound

This paper cites Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.792365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.119865Z digest=sha256:d813301290a934eb953893f14f45c0c550485bd2501136b63e02ca89261acc0d

Observation 845aec60-a30a-4e35-853d-5fa9eba6fcab · outbound

This paper cites Lookaround optimizer: k steps around, 1 step average.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Lookaround optimizer: k steps around, 1 step average

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.684036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.173871Z digest=sha256:cf46af6ee865b7c01561048d3c6931a78fc8d8caf71c6b01f907e3d9c043c342

Observation d294c5d1-9d3c-4f54-bda2-74f1865a1541 · outbound

This paper cites Lookahead optimizer: k steps forward, 1 step back.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Lookahead optimizer: k steps forward, 1 step back

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.583776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.222044Z digest=sha256:8543a215515afa723b1b5c6252038fea3feecc19a298772d2a28c3344da736bb

Observation b1556a4e-f8bb-4b17-ab55-6204ff360efb · outbound

This paper cites Theoretical bound-guided hierarchical vae for neural image codecs.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Theoretical bound-guided hierarchical vae for neural image codecs

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.478352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.291133Z digest=sha256:42a30be057d96f455ceb2da6061b48cdae433d2c114f05f62a44481de8afda20

Observation 2ec2786e-9028-4d4d-acaa-2417cbbcf371 · outbound

This paper cites Another way to the top: Exploit contextual clustering in learned image coding.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Another way to the top: Exploit contextual clustering in learned image coding

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.391911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.336487Z digest=sha256:fcbe0aca2a2ea49f204b26a5a0c3925b54488580b93bac3d14d7412b42fbffd7

Observation b2abd893-95b4-490e-8bf1-fcdca99b1698 · outbound

This paper cites On efficient neural network architectures for image compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics On efficient neural network architectures for image compression

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.271765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.381083Z digest=sha256:cfa311d74324bbf9f6c565bda14f33e1f27a6a2da20f9cd1354f5555568992f3

Observation 9a8af3fc-9716-4b75-82aa-d9c6f91b6468 · outbound

This paper cites Balanced Rate-Distortion Optimization in Learned Image Compression.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Balanced Rate-Distortion Optimization in Learned Image Compression

Reference 90

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:40:27.757661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.435102Z digest=sha256:32999251833133b7d085816e3d84ed64ba766e0fe17850a9318834f0a26636f5

Observation 8daf2183-09fc-473c-98be-e8a9f2850eb2 · outbound

This paper cites Enhanced screen content image compression: A synergistic approach for structural fidelity and text integrity preservation.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Enhanced screen content image compression: A synergistic approach for structural fidelity and text integrity preservation

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:28.162132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.501730Z digest=sha256:9a1e2f1fa3a9e4bd4d16a8de7d3c289a417d16d1415afc3d3c417dfd0a05b48f

Observation ff0d83dc-870e-4876-b4cc-57850785ed56 · outbound

This paper cites Towards understanding why lookahead generalizes better than sgd and beyond.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Towards understanding why lookahead generalizes better than sgd and beyond

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:27.987058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.547115Z digest=sha256:53b4dffaecaa9f16276181e7a013321ac080c1e5be1abb23dd85e69efeaa5233

Observation e3ac9312-fbfb-4065-ac84-94895ee0d56c · outbound

This paper cites Efficient neural network training via forward and backward propagation sparsification.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Efficient neural network training via forward and backward propagation sparsification

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:40:27.880464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:40:27.599102Z digest=sha256:6a1db6e1e584b97f48220b97b2cbbdbb81e0182b889a0ef5c5a4e9f8da965b1f

Observation bafaa457-efc9-4e13-b75a-65f067d23a27 · outbound

This paper cites write newline.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics write newline

Reference 94

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:27.654568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:40:27.654568Z digest=sha256:8828db169756104a89afb04276e4acb131a241eec63d99623fd60fa58694ea01

Pith citing papers

No inbound Pith citation observations are available.