Accelerating Learned Image Compression Through Modeling Neural Training Dynamics | Paper Citation Record

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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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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This paper cites Nonlinear transform coding.

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

Reference 3

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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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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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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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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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This paper cites Bj ntegaard.

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics Bj ntegaard

Reference 8

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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This paper cites Momentum contrast for unsupervised visual representation learning.

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

Reference 26

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This paper cites Lo RA : Low-rank adaptation of large language models.

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

Reference 27

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This paper cites JPEG AI Common Training & Test Conditions v8.0.

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

Reference 28

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This paper cites Garipov, Dmitry P.

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

Reference 29

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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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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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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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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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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

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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

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This paper cites Deep contextual video compression.

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

Reference 39

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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

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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

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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

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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

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This paper cites Subspace adversarial training.

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

Reference 44

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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

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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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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

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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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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 2026-08-17T06:30:58.91139+00:00.

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

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