Smooth Model Compression without Fine-Tuning | Paper Citation Record

Observation a92dd1a5-6e62-40f3-89c5-21273d1f2464 · outbound

This paper cites Online embedding compression for text classification using low rank matrix factorization.

Smooth Model Compression without Fine-Tuning Online embedding compression for text classification using low rank matrix factorization

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:18.201092Z

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Observation 22feda92-49c6-420b-8e81-5733894f4ebf · outbound

This paper cites Fluctuation-based adaptive structured pruning for large language models.

Smooth Model Compression without Fine-Tuning Fluctuation-based adaptive structured pruning for large language models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:17.984589Z

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Observation c6b31d9b-53b6-4dc0-8c4f-ebd765cba2f0 · outbound

This paper cites On gradient regularizers for mmd gans.Advances in neural information processing systems, 31, 2018.

Smooth Model Compression without Fine-Tuning On gradient regularizers for mmd gans.Advances in neural information processing systems, 31, 2018

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:17.773468Z

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Observation 9992f729-6058-4e8a-8744-736f65062e2f · outbound

This paper cites Slicegpt: Compress large language models by deleting rows and columns.

Smooth Model Compression without Fine-Tuning Slicegpt: Compress large language models by deleting rows and columns

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:17.599730Z

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Observation 8e953837-908b-40ee-821e-c5dc1904f095 · outbound

This paper cites Representing smooth functions as compositions of near-identity functions with implications for deep network optimization.

Smooth Model Compression without Fine-Tuning Representing smooth functions as compositions of near-identity functions with implications for deep network optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:06.566686Z

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Observation 14a5ac12-b72d-4eeb-b901-219be14c09da · outbound

This paper cites Invertible residual networks.

Smooth Model Compression without Fine-Tuning Invertible residual networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:17.380637Z

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Observation d83c6c19-4989-49fa-9427-1bb37040006f · outbound

This paper cites A Survey of Model Compression and Acceleration for Deep Neural Networks.

Smooth Model Compression without Fine-Tuning A Survey of Model Compression and Acceleration for Deep Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:06.748067Z

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Observation 6b097332-307f-4129-bb05-e3902a0bfb6e · outbound

This paper cites Parseval networks: Improving robustness to adversarial examples.

Smooth Model Compression without Fine-Tuning Parseval networks: Improving robustness to adversarial examples

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:06.853913Z

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Observation edb10441-f4ed-4f3c-8c13-32250f7e9f16 · outbound

This paper cites Hawq: Hessian aware quantization of neural networks with mixed-precision.

Smooth Model Compression without Fine-Tuning Hawq: Hessian aware quantization of neural networks with mixed-precision

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:06.967288Z

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Observation 080d3db6-6746-4310-91d9-d5dd0b8f93d6 · outbound

This paper cites Improving generalization performance using double backpropagation.

Smooth Model Compression without Fine-Tuning Improving generalization performance using double backpropagation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:17.159202Z

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Observation bb6abfdf-2676-4375-b078-d8eb7938c0dc · outbound

This paper cites The role of permutation invariance in linear mode connectivity of neural networks.

Smooth Model Compression without Fine-Tuning The role of permutation invariance in linear mode connectivity of neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:16.969197Z

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Observation 6fbd3b2e-dc0a-4c48-8558-0e2c8c43a40e · outbound

This paper cites Neural scene representation and rendering.Science, 360(6394):1204–1210, 2018.

Smooth Model Compression without Fine-Tuning Neural scene representation and rendering.Science, 360(6394):1204–1210, 2018

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:16.799934Z

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Observation d6ceedb8-2b9f-4386-994e-6c4b5bd36f19 · outbound

This paper cites Many paths to equilibrium: Gans do not need to decrease a divergence at every step.

Smooth Model Compression without Fine-Tuning Many paths to equilibrium: Gans do not need to decrease a divergence at every step

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:16.661142Z

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Observation 119d68c9-b227-4c73-9f0f-a0b7b49cc789 · outbound

This paper cites Learning a smooth kernel regularizer for convolutional neural networks.

Smooth Model Compression without Fine-Tuning Learning a smooth kernel regularizer for convolutional neural networks

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:30:12.875128Z

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Observation 0cb40509-e00a-4732-977e-8ae5cd3d3ce1 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Smooth Model Compression without Fine-Tuning The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:07.675759Z

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Observation 5fba65e3-ca99-4396-a793-423a4cf80f28 · outbound

This paper cites Optimal brain compression: A framework for accurate post-training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475–4488, 2022.

Smooth Model Compression without Fine-Tuning Optimal brain compression: A framework for accurate post-training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475–4488, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:07.763259Z

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Observation e017a3c4-a969-45c9-a433-255e8452f36d · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

Smooth Model Compression without Fine-Tuning Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:07.886636Z

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Observation 60eac7ac-ce5e-43d5-b768-28cf08beaf05 · outbound

This paper cites Born again neural networks.

Smooth Model Compression without Fine-Tuning Born again neural networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:07.982926Z

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Observation 1f1aeecc-5769-449f-835e-6efd1f155fb0 · outbound

This paper cites Stochastic training is not necessary for generalization.

Smooth Model Compression without Fine-Tuning Stochastic training is not necessary for generalization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:16.505909Z

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Observation 2805b455-d6d7-49a3-9e13-f1ab1a5be346 · outbound

This paper cites Knowledge distillation: A survey.

Smooth Model Compression without Fine-Tuning Knowledge distillation: A survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.161699Z

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Observation a85b16aa-c670-40c0-850e-ac7cac6d467d · outbound

This paper cites Regularisation of neural networks by enforcing lipschitz continuity.Machine Learning, 110:393–416, 2021.

Smooth Model Compression without Fine-Tuning Regularisation of neural networks by enforcing lipschitz continuity.Machine Learning, 110:393–416, 2021

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:16.406220Z

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Observation d2dce5cc-2ce6-425b-84a7-835126f0f7b8 · outbound

This paper cites Improved training of wasserstein gans.Advances in neural information processing systems, 30, 2017.

Smooth Model Compression without Fine-Tuning Improved training of wasserstein gans.Advances in neural information processing systems, 30, 2017

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.370851Z

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Observation c2942940-5a6a-466a-83f4-1aeb25cb85c2 · outbound

This paper cites Learning both weights and connections for efficient neural network.Advances in neural information processing systems, 28, 2015.

Smooth Model Compression without Fine-Tuning Learning both weights and connections for efficient neural network.Advances in neural information processing systems, 28, 2015

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.456628Z

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Observation f1558d3c-afb2-4b1c-94e7-b17ce02b5c89 · outbound

This paper cites Optimal brain surgeon and general network pruning.

Smooth Model Compression without Fine-Tuning Optimal brain surgeon and general network pruning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.519624Z

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Observation 2e695d43-01b3-4361-bf83-7d4b7b6519fa · outbound

This paper cites Deep residual learning for image recognition.

Smooth Model Compression without Fine-Tuning Deep residual learning for image recognition

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.603463Z

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Observation edc6e669-d148-4204-8180-192acfadff02 · outbound

This paper cites Bag of tricks for image classification with convolutional neural networks.

Smooth Model Compression without Fine-Tuning Bag of tricks for image classification with convolutional neural networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.717050Z

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Observation 16a86be1-e712-4000-9912-a503f91ebcf2 · outbound

This paper cites Multi-task zipping via layer-wise neuron sharing.Advances in Neural Information Processing Systems, 31, 2018.

Smooth Model Compression without Fine-Tuning Multi-task zipping via layer-wise neuron sharing.Advances in Neural Information Processing Systems, 31, 2018

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:16.249679Z

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Observation 81fc4488-3194-48c1-814e-2622eb94e84a · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

Smooth Model Compression without Fine-Tuning Channel pruning for accelerating very deep neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.887893Z

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Observation 3ac37b63-8e69-40e8-a628-054d4803b5a6 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Smooth Model Compression without Fine-Tuning Distilling the Knowledge in a Neural Network

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:08.964381Z

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Observation 278b3488-f7c5-4f7d-bbc2-e767d1ea16a7 · outbound

This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

Smooth Model Compression without Fine-Tuning Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.113034Z

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Observation 50306162-f995-4dc7-bcaf-c0e126f6d384 · outbound

This paper cites Quantized neural networks: Training neural networks with low precision weights and activations.journal of machine learning research, 18(187):1–30, 2018.

Smooth Model Compression without Fine-Tuning Quantized neural networks: Training neural networks with low precision weights and activations.journal of machine learning research, 18(187):1–30, 2018

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:16.100846Z

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Observation b1ddef33-b2ee-4d5b-a722-13c0aa012942 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526, 2017.

Smooth Model Compression without Fine-Tuning Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526, 2017

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.296570Z

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Observation 4c1e4203-a757-4b23-becf-199967b27638 · outbound

This paper cites Literature survey on low rank approximation of matrices.Linear and Multilinear Algebra, 65(11):2212–2244, 2017.

Smooth Model Compression without Fine-Tuning Literature survey on low rank approximation of matrices.Linear and Multilinear Algebra, 65(11):2212–2244, 2017

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:15.972351Z

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Observation 155468eb-1581-4e90-9792-e0ccacdf2cb1 · outbound

This paper cites On Convergence and Stability of GANs.

Smooth Model Compression without Fine-Tuning On Convergence and Stability of GANs

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.537422Z

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Observation 20f82b6f-6b8e-4cb2-a990-f2829bcdd852 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Smooth Model Compression without Fine-Tuning Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.614186Z

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Observation 14a1decc-99dc-4827-847b-b161e63961b3 · outbound

This paper cites Learning multiple layers of features from tiny images.

Smooth Model Compression without Fine-Tuning Learning multiple layers of features from tiny images

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.697478Z

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Observation 9453502e-eab0-4fdf-9c83-20ba5658946e · outbound

This paper cites A fast post-training pruning framework for transformers.Advances in Neural Information Processing Systems, 35:24101–24116, 2022.

Smooth Model Compression without Fine-Tuning A fast post-training pruning framework for transformers.Advances in Neural Information Processing Systems, 35:24101–24116, 2022

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:15.821019Z

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Observation 0ecd77aa-0b9a-47fd-a1c3-7f7fc06bc0b6 · outbound

This paper cites Optimal brain damage.Advances in neural information processing systems, 2, 1989.

Smooth Model Compression without Fine-Tuning Optimal brain damage.Advances in neural information processing systems, 2, 1989

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.839494Z

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Observation 643916e0-bb91-444b-883a-d4957feedbe5 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Smooth Model Compression without Fine-Tuning Pruning Filters for Efficient ConvNets

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.904070Z

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Observation ab477372-9a03-4029-a903-ec9f3da9766a · outbound

This paper cites Lightweight deep learning for resource-constrained environments: A survey.ACM Computing Surveys, 56(10):1–42, 2024.

Smooth Model Compression without Fine-Tuning Lightweight deep learning for resource-constrained environments: A survey.ACM Computing Surveys, 56(10):1–42, 2024

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:09.971519Z

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Observation fbe44390-9a28-497f-910c-3ca16038f2ce · outbound

This paper cites Decoupled Weight Decay Regularization.

Smooth Model Compression without Fine-Tuning Decoupled Weight Decay Regularization

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:10.045463Z

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Observation 832dd1f2-c743-4925-84dd-7d6e71a558ca · outbound

This paper cites Thinet: A filter level pruning method for deep neural network compression.

Smooth Model Compression without Fine-Tuning Thinet: A filter level pruning method for deep neural network compression

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:10.107195Z

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Observation 356f85e1-a875-4683-9288-d2d161d1c280 · outbound

This paper cites Shortgpt: Layers in large language models are more redundant than you expect.CoRR, 2024.

Smooth Model Compression without Fine-Tuning Shortgpt: Layers in large language models are more redundant than you expect.CoRR, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:15.668493Z

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Observation 823245af-8343-4f3f-9163-65b24bdf3767 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65 (1):99–106, 2021.

Smooth Model Compression without Fine-Tuning Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65 (1):99–106, 2021

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:10.243781Z

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Observation 5ce90e4c-571c-4785-820b-960ce7ef4a52 · outbound

This paper cites Filter pruning using hierarchical group sparse regularization for deep convolutional neural networks.

Smooth Model Compression without Fine-Tuning Filter pruning using hierarchical group sparse regularization for deep convolutional neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:15.502536Z

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Observation 2c92017a-51b0-415a-9ebd-481e973f9008 · outbound

This paper cites Spectral normalization for generative adversarial networks.

Smooth Model Compression without Fine-Tuning Spectral normalization for generative adversarial networks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:10.415504Z

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Observation b09925d0-381a-4ccc-b075-b11cbf8355bf · outbound

This paper cites Sosp: Efficiently capturing global correlations by second-order structured pruning.

Smooth Model Compression without Fine-Tuning Sosp: Efficiently capturing global correlations by second-order structured pruning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:15.320874Z

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Observation f700f187-793d-4c54-820f-ed92befaaf7e · outbound

This paper cites Collaborative channel pruning for deep networks.

Smooth Model Compression without Fine-Tuning Collaborative channel pruning for deep networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:15.075082Z

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Observation df3ca50e-f62b-46c6-b574-26bf41002eed · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

Smooth Model Compression without Fine-Tuning FitNets: Hints for Thin Deep Nets

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:10.651644Z

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Observation 1cb00309-c50c-44c2-b89b-aa9e0bb36dc4 · outbound

This paper cites Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization.

Smooth Model Compression without Fine-Tuning Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:30:12.603457Z

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Observation 29a03df0-35dd-4ba8-afda-e54c76cc33d4 · outbound

This paper cites Wire: Wavelet implicit neural representations.

Smooth Model Compression without Fine-Tuning Wire: Wavelet implicit neural representations

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:10.834505Z

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Observation 9b27811b-2589-4bc3-9c50-da7ab81a02b3 · outbound

This paper cites The singular values of convolutional layers.

Smooth Model Compression without Fine-Tuning The singular values of convolutional layers

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:14.791545Z

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Observation dfea235f-af04-45ac-bab3-68399366dcc3 · outbound

This paper cites Implicit neural representations with periodic activation functions.Advances in neural information processing systems, 33:7462–7473, 2020.

Smooth Model Compression without Fine-Tuning Implicit neural representations with periodic activation functions.Advances in neural information processing systems, 33:7462–7473, 2020

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:11.011353Z

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Observation 07ecfab4-9f10-4b8c-a693-46d845415260 · outbound

This paper cites Robust large margin deep neural networks.IEEE Transactions on Signal Processing, 65(16):4265–4280, 2017.

Smooth Model Compression without Fine-Tuning Robust large margin deep neural networks.IEEE Transactions on Signal Processing, 65(16):4265–4280, 2017

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:14.502950Z

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Observation de6c4a04-eef4-4a70-a377-14e36edd5d61 · outbound

This paper cites Integral neural networks.

Smooth Model Compression without Fine-Tuning Integral neural networks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:11.233891Z

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Observation 25b8250a-658a-4321-81c2-8639e06aadfb · outbound

This paper cites A simple and effective pruning approach for large language models.

Smooth Model Compression without Fine-Tuning A simple and effective pruning approach for large language models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:11.329093Z

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Observation 9c0e4e31-695c-4a2a-b375-1243bae04866 · outbound

This paper cites Towards meta-pruning via optimal transport.

Smooth Model Compression without Fine-Tuning Towards meta-pruning via optimal transport

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:14.289511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cd7281f9-c8a9-4bf1-8203-2d8381bd874d · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Smooth Model Compression without Fine-Tuning Training data-efficient image transformers & distillation through attention

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:11.525199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef627779-e12f-43a3-a798-80e9bbac1e99 · outbound

This paper cites Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks.Advances in neural information processing systems, 31, 2018.

Smooth Model Compression without Fine-Tuning Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks.Advances in neural information processing systems, 31, 2018

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:14.107705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 806788f4-8af9-4435-9ae4-2c1fc976bb1e · outbound

This paper cites Forget the data and fine-tuning! just fold the network to compress.

Smooth Model Compression without Fine-Tuning Forget the data and fine-tuning! just fold the network to compress

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:13.951007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d4091609-2533-4788-9a25-fb0ca7063412 · outbound

This paper cites Learning structured sparsity in deep neural networks.Advances in neural information processing systems, 29, 2016.

Smooth Model Compression without Fine-Tuning Learning structured sparsity in deep neural networks.Advances in neural information processing systems, 29, 2016

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:11.753936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ea15573-6496-4653-9c74-28f6bedf26cc · outbound

This paper cites Model pruning based on filter similarity for edge device deployment.Frontiers in Neurorobotics, 17:1132679, 2023.

Smooth Model Compression without Fine-Tuning Model pruning based on filter similarity for edge device deployment.Frontiers in Neurorobotics, 17:1132679, 2023

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:13.662753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9e962da-cdbe-41c4-b307-6ebef71aa6a3 · outbound

This paper cites Neural metamorphosis.

Smooth Model Compression without Fine-Tuning Neural metamorphosis

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:13.538403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ccb613b8-5cb5-40a6-b236-d6c3a3fa6b81 · outbound

This paper cites Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers.

Smooth Model Compression without Fine-Tuning Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:13.400378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 31d164b4-ec61-48b2-87a1-9bed53a19259 · outbound

This paper cites Spectral Norm Regularization for Improving the Generalizability of Deep Learning.

Smooth Model Compression without Fine-Tuning Spectral Norm Regularization for Improving the Generalizability of Deep Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:12.050286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbbf23a1-e817-406c-8b58-35470c7015ac · outbound

This paper cites Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks.Advances in neural information processing systems, 32, 2019.

Smooth Model Compression without Fine-Tuning Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks.Advances in neural information processing systems, 32, 2019

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:13.252857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 19c37776-a671-4ed0-9834-b4c32ed4a507 · outbound

This paper cites On compressing deep models by low rank and sparse decomposition.

Smooth Model Compression without Fine-Tuning On compressing deep models by low rank and sparse decomposition

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:30:13.076371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 310d3b94-e89e-482a-b1fe-8a4fb9a189f9 · outbound

This paper cites Be your own teacher: Improve the performance of convolutional neural networks via self distillation.

Smooth Model Compression without Fine-Tuning Be your own teacher: Improve the performance of convolutional neural networks via self distillation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T12:30:12.323559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b59b0ab9-b085-4985-9e5d-5f5a6a4b91f7 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Smooth Model Compression without Fine-Tuning DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 69

Resolution: malformed identifier; no resolver link, observed 2026-08-07T12:30:12.402648Z

Source-reported events for the cited work

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