Layer Pruning with Consensus: A Triple-Win Solution | Paper Citation Record

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This paper cites DECORE: deep compression with reinforce- ment learning.

Layer Pruning with Consensus: A Triple-Win Solution DECORE: deep compression with reinforce- ment learning

Reference 1

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Observation a536cc2c-2738-4346-ba2c-591c84049cc6 · outbound

This paper cites an unresolved cited work.

Layer Pruning with Consensus: A Triple-Win Solution Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:19:51.714608Z

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Observation 043c473e-a6fc-44bd-9c41-f66d0e6aba3a · outbound

This paper cites Bartoldson, Ari S.

Layer Pruning with Consensus: A Triple-Win Solution Bartoldson, Ari S

Reference 3

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Observation 96578257-0bc1-4ea0-9614-56d6f25be0a5 · outbound

This paper cites Shallowing deep networks: Layer-wise pruning based on feature representa- tions.

Layer Pruning with Consensus: A Triple-Win Solution Shallowing deep networks: Layer-wise pruning based on feature representa- tions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.648032Z

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Observation ca5500ac-9806-4b8e-b5ec-619348f564df · outbound

This paper cites Dynamical channel pruning by conditional accuracy change for deep neural networks.

Layer Pruning with Consensus: A Triple-Win Solution Dynamical channel pruning by conditional accuracy change for deep neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.618238Z

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Observation 7de62fb0-48dc-498e-a49b-309ba11da3a6 · outbound

This paper cites A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommen- dations.

Layer Pruning with Consensus: A Triple-Win Solution A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommen- dations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.580304Z

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Observation c40ecd32-f28c-4f01-a379-629f81695d4c · outbound

This paper cites The efficiency mis- nomer.

Layer Pruning with Consensus: A Triple-Win Solution The efficiency mis- nomer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.543722Z

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Observation 28905082-d5a4-420a-b59c-4e71358080d8 · outbound

This paper cites Attention is not all you need: pure attention loses rank doubly exponentially with depth.

Layer Pruning with Consensus: A Triple-Win Solution Attention is not all you need: pure attention loses rank doubly exponentially with depth

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.516217Z

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Observation 23c8d423-d385-4f95-99da-10e857e87647 · outbound

This paper cites Layer folding: Neu- ral network depth reduction using activation lin- earization.

Layer Pruning with Consensus: A Triple-Win Solution Layer folding: Neu- ral network depth reduction using activation lin- earization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.480183Z

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Observation fef7097f-794d-41dc-b5c9-6d7cbb46f300 · outbound

This paper cites Duong and et al.

Layer Pruning with Consensus: A Triple-Win Solution Duong and et al

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.424916Z

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Observation 04b337db-223f-4e10-8fa0-7becf0e6da1a · outbound

This paper cites an unresolved cited work.

Layer Pruning with Consensus: A Triple-Win Solution Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:19:51.387767Z

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Observation ffd3247c-6268-4e1b-8125-3a8a811d4a4c · outbound

This paper cites Llmcarbon: Modeling the end-to-end carbon foot- print of large language models.

Layer Pruning with Consensus: A Triple-Win Solution Llmcarbon: Modeling the end-to-end carbon foot- print of large language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.348886Z

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Observation 5602010e-b283-4bf0-b4d5-abfaeac35e6c · outbound

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

Layer Pruning with Consensus: A Triple-Win Solution Sparsegpt: Mas- sive language models can be accurately pruned in one-shot

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.330044Z

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Observation ab86bc0d-945a-4858-8eec-96df8b645a74 · outbound

This paper cites Depthshrinker: A new com- pression paradigm towards boosting real-hardware efficiency of compact neural networks.

Layer Pruning with Consensus: A Triple-Win Solution Depthshrinker: A new com- pression paradigm towards boosting real-hardware efficiency of compact neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.225575Z

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Observation cd0d012f-2999-4f90-889f-665bd83f7c82 · outbound

This paper cites Jointly training and pruning cnns via learnable agent guidance and alignment.

Layer Pruning with Consensus: A Triple-Win Solution Jointly training and pruning cnns via learnable agent guidance and alignment

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.182964Z

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Observation 02064ec8-3951-4861-be72-ce5c418a8c13 · outbound

This paper cites Bilevelpruning: Unified dynamic and static channel pruning for convolutional neu- ral networks.

Layer Pruning with Consensus: A Triple-Win Solution Bilevelpruning: Unified dynamic and static channel pruning for convolutional neu- ral networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.150241Z

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Observation 874ecc6d-f4f5-4cc9-a866-def5563a0ffa · outbound

This paper cites Shortcut learning in deep neural networks.

Layer Pruning with Consensus: A Triple-Win Solution Shortcut learning in deep neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.113853Z

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Observation da38632d-6777-4c0a-a8e1-766e758a9c48 · outbound

This paper cites DAIS: automatic chan- nel pruning via differentiable annealing indicator search.

Layer Pruning with Consensus: A Triple-Win Solution DAIS: automatic chan- nel pruning via differentiable annealing indicator search

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.080061Z

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Observation cb4558f3-7c73-4e57-9e42-1597aa21535e · outbound

This paper cites Blending pruning criteria for convolutional neural networks.

Layer Pruning with Consensus: A Triple-Win Solution Blending pruning criteria for convolutional neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:51.034235Z

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Observation a553edb6-95ae-48c4-b353-841036ad2527 · outbound

This paper cites Structured pruning for deep convolutional neural networks: A survey.

Layer Pruning with Consensus: A Triple-Win Solution Structured pruning for deep convolutional neural networks: A survey

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.987365Z

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Observation 13a46510-6c43-4eb1-b7d5-3757e6d0c3a7 · outbound

This paper cites Dietterich.

Layer Pruning with Consensus: A Triple-Win Solution Dietterich

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.936397Z

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Observation 98f9008a-ae75-4239-9fcb-bb7daae83279 · outbound

This paper cites Pixmix: Dreamlike pic- tures comprehensively improve safety measures.

Layer Pruning with Consensus: A Triple-Win Solution Pixmix: Dreamlike pic- tures comprehensively improve safety measures

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.909270Z

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Observation bd5635b1-5b93-496a-a8b7-11380de47191 · outbound

This paper cites Hermann and et al.

Layer Pruning with Consensus: A Triple-Win Solution Hermann and et al

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.877810Z

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Observation cabbe051-a3a6-4ec2-ba6b-e32d7893ea58 · outbound

This paper cites Deep networks with stochas- tic depth.

Layer Pruning with Consensus: A Triple-Win Solution Deep networks with stochas- tic depth

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.849850Z

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Observation 2ae870da-1d5e-4ca8-bd4c-1e9e493ddfff · outbound

This paper cites Rethinking the prun- ing criteria for convolutional neural network.

Layer Pruning with Consensus: A Triple-Win Solution Rethinking the prun- ing criteria for convolutional neural network

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.807722Z

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Observation d693a871-902f-468d-805a-f5c3e9660dea · outbound

This paper cites On the channel pruning using graph convolution network for convolutional neural network acceleration.

Layer Pruning with Consensus: A Triple-Win Solution On the channel pruning using graph convolution network for convolutional neural network acceleration

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.771503Z

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Observation 75a6cf32-adb6-44e6-ace3-9fad9298c4c6 · outbound

This paper cites Discriminative layer prun- ing for convolutional neural networks.

Layer Pruning with Consensus: A Triple-Win Solution Discriminative layer prun- ing for convolutional neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.721327Z

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Observation dc63e71d-6069-4d4d-84ee-0c88e3da45c5 · outbound

This paper cites When layers play the lottery, all tickets win at initialization.

Layer Pruning with Consensus: A Triple-Win Solution When layers play the lottery, all tickets win at initialization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.645891Z

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Observation e19e9314-d9ae-4413-87bc-2355ba22b8b9 · outbound

This paper cites On the effect of pruning on adversarial robustness.

Layer Pruning with Consensus: A Triple-Win Solution On the effect of pruning on adversarial robustness

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.553598Z

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Observation 32f7046e-1abf-475f-ae26-0d215649287e · outbound

This paper cites Shortened llama: A simple depth pruning for large language models.

Layer Pruning with Consensus: A Triple-Win Solution Shortened llama: A simple depth pruning for large language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.495716Z

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Observation f339beb7-531a-4a78-8c02-f447b2f74d12 · outbound

This paper cites Last layer re-training is suf- ficient for robustness to spurious correlations.

Layer Pruning with Consensus: A Triple-Win Solution Last layer re-training is suf- ficient for robustness to spurious correlations

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.450193Z

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Observation 135c5a6e-6931-4f03-90f0-b4182b2c27f7 · outbound

This paper cites Ma- honey, Joseph Hassoun, Kurt Keutzer, and Amir Gholami.

Layer Pruning with Consensus: A Triple-Win Solution Ma- honey, Joseph Hassoun, Kurt Keutzer, and Amir Gholami

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.400471Z

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Observation 1bfc175f-8ef2-4fb0-ada2-5406dfa24193 · outbound

This paper cites Quantifying the carbon emissions of machine learning.

Layer Pruning with Consensus: A Triple-Win Solution Quantifying the carbon emissions of machine learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.344273Z

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Observation 7302cb59-101b-48d6-bcd8-6f35f1661aa3 · outbound

This paper cites Can pruning improve certi- fied robustness of neural networks? TMLR, 2023.

Layer Pruning with Consensus: A Triple-Win Solution Can pruning improve certi- fied robustness of neural networks? TMLR, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.298358Z

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Observation 02cc3fda-af25-4296-919d-f84f6035b7c4 · outbound

This paper cites Pruning networks with cross-layer ranking & k-reciprocal nearest filters.

Layer Pruning with Consensus: A Triple-Win Solution Pruning networks with cross-layer ranking & k-reciprocal nearest filters

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.192674Z

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Observation 959e77d2-19ac-4daa-9ea2-196096e36575 · outbound

This paper cites Hrank: Filter pruning using high-rank feature map.

Layer Pruning with Consensus: A Triple-Win Solution Hrank: Filter pruning using high-rank feature map

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.117108Z

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Observation d65036cd-ad46-4278-ae4e-728a9f97de8f · outbound

This paper cites SOKS: automatic searching of the optimal kernel shapes for stripe-wise network pruning.

Layer Pruning with Consensus: A Triple-Win Solution SOKS: automatic searching of the optimal kernel shapes for stripe-wise network pruning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:50.025892Z

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Observation 5967d7d2-522e-4bd2-bb5d-30e976925d71 · outbound

This paper cites UPDP: A unified progressive depth pruner for CNN and vision transformer.

Layer Pruning with Consensus: A Triple-Win Solution UPDP: A unified progressive depth pruner for CNN and vision transformer

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.928408Z

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Observation 4602dba2-46ee-4103-8610-7a7f5dfc9fb9 · outbound

This paper cites Harder or different? a closer look at distribution shift in dataset repro- duction.

Layer Pruning with Consensus: A Triple-Win Solution Harder or different? a closer look at distribution shift in dataset repro- duction

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.876867Z

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Observation b3c4bf68-26b9-4907-a0b3-4a4908a37b20 · outbound

This paper cites LLM-pruner: On the structural pruning of large language models.

Layer Pruning with Consensus: A Triple-Win Solution LLM-pruner: On the structural pruning of large language models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.847799Z

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Observation b23e50e7-6d68-4787-8ec5-f06b83ea9fb7 · outbound

This paper cites The tunnel effect: Building data representations in deep neural net- works.

Layer Pruning with Consensus: A Triple-Win Solution The tunnel effect: Building data representations in deep neural net- works

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.806235Z

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Observation 6edfa13e-f131-4982-b208-f56d8af71595 · outbound

This paper cites What makes a good prune? maximal unstructured pruning for maximal cosine similarity.

Layer Pruning with Consensus: A Triple-Win Solution What makes a good prune? maximal unstructured pruning for maximal cosine similarity

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.742754Z

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Observation c03d2f4c-f6c8-4a7d-93f9-10c0ce6aad51 · outbound

This paper cites Investigating calibration and corruption robust- ness of post-hoc pruned perception cnns: An im- age classification benchmark study.

Layer Pruning with Consensus: A Triple-Win Solution Investigating calibration and corruption robust- ness of post-hoc pruned perception cnns: An im- age classification benchmark study

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.687630Z

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Observation 525c9227-cd3d-404a-822c-2bb9a147d7e8 · outbound

This paper cites SOSP: efficiently captur- ing global correlations by second-order structured pruning.

Layer Pruning with Consensus: A Triple-Win Solution SOSP: efficiently captur- ing global correlations by second-order structured pruning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.643974Z

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Observation 74362ce2-79db-4cda-b3f5-8f4893fe3834 · outbound

This paper cites An introduction to adversarially robust deep learning.

Layer Pruning with Consensus: A Triple-Win Solution An introduction to adversarially robust deep learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.610555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 07e833a5-7265-4182-93f9-ec4600b99f64 · outbound

This paper cites an unresolved cited work.

Layer Pruning with Consensus: A Triple-Win Solution Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:19:49.584686Z

Source-reported events for the cited work

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Observation 36ce54fb-f7f7-4b12-a394-46ba5913b919 · outbound

This paper cites Effective Layer Pruning Through Similarity Metric Perspective.

Layer Pruning with Consensus: A Triple-Win Solution Effective Layer Pruning Through Similarity Metric Perspective

Reference 47

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T15:19:48.528535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1157b4f0-bbbb-4d98-940d-2ca031405cd7 · outbound

This paper cites Smith, and Oren Etzioni.

Layer Pruning with Consensus: A Triple-Win Solution Smith, and Oren Etzioni

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.549952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4a941a1e-51dc-4d23-9675-14979c41b9ad · outbound

This paper cites Human activity recognition based on smartphone and wearable sensors using multiscale dcnn ensemble.

Layer Pruning with Consensus: A Triple-Win Solution Human activity recognition based on smartphone and wearable sensors using multiscale dcnn ensemble

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.515319Z

Source-reported events for the cited work

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Observation 6d74e088-802c-438f-aff2-adcc4065e6aa · outbound

This paper cites ´Alvarez.

Layer Pruning with Consensus: A Triple-Win Solution ´Alvarez

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.476889Z

Source-reported events for the cited work

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Observation d7bcf5f0-ade5-46fd-bd36-2d80c93cf731 · outbound

This paper cites Energy and policy considerations for deep learning in NLP.

Layer Pruning with Consensus: A Triple-Win Solution Energy and policy considerations for deep learning in NLP

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.440781Z

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Observation 70fed6b3-b5f5-4a19-bb31-b59442072b13 · outbound

This paper cites Zico Kolter.

Layer Pruning with Consensus: A Triple-Win Solution Zico Kolter

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.417197Z

Source-reported events for the cited work

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Observation f843c5d7-e009-46e4-b1c0-94039abc81a5 · outbound

This paper cites Llama: Open and efficient foundation language models.

Layer Pruning with Consensus: A Triple-Win Solution Llama: Open and efficient foundation language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.383049Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8176dae5-7551-4eaa-9512-daa78e33ae73 · outbound

This paper cites Mobileone: An improved one millisecond mobile backbone.

Layer Pruning with Consensus: A Triple-Win Solution Mobileone: An improved one millisecond mobile backbone

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.330869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e8c7bcd4-a935-46e8-8aea-be4e0b3b41ad · outbound

This paper cites Wilber, and Serge J.

Layer Pruning with Consensus: A Triple-Win Solution Wilber, and Serge J

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.289355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a4e0dec4-10ef-408e-8984-d167c120f99c · outbound

This paper cites Recent advances on neural network prun- ing at initialization.

Layer Pruning with Consensus: A Triple-Win Solution Recent advances on neural network prun- ing at initialization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.231523Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a5c54fef-98a2-4d43-9bf1-bcc7cf3e7c1e · outbound

This paper cites Channel pruning via lookahead search guided reinforcement learn- ing.

Layer Pruning with Consensus: A Triple-Win Solution Channel pruning via lookahead search guided reinforcement learn- ing

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.196991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a8e555e5-11d5-45bc-a4c2-c93b9c950b29 · outbound

This paper cites Generalized shape metrics on neural representations.

Layer Pruning with Consensus: A Triple-Win Solution Generalized shape metrics on neural representations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.145264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b7d8a51-1067-43cc-bd34-185f131403cc · outbound

This paper cites Auto-train-once: Con- troller network guided automatic network pruning from scratch.

Layer Pruning with Consensus: A Triple-Win Solution Auto-train-once: Con- troller network guided automatic network pruning from scratch

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.113094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 18721321-eabf-4905-8e81-caed88d75350 · outbound

This paper cites Sheared llama: Accelerating lan- guage model pre-training via structured pruning.

Layer Pruning with Consensus: A Triple-Win Solution Sheared llama: Accelerating lan- guage model pre-training via structured pruning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.067337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b067576d-ef5b-4310-bd91-ee2a1b7c3dbe · outbound

This paper cites Imagenet-OOD: Deciphering modern out-of-distribution detection algorithms.

Layer Pruning with Consensus: A Triple-Win Solution Imagenet-OOD: Deciphering modern out-of-distribution detection algorithms

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:49.023006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eeddb151-ddd2-4604-a2de-401e67facabb · outbound

This paper cites Auto graph encoder-decoder for neural network pruning.

Layer Pruning with Consensus: A Triple-Win Solution Auto graph encoder-decoder for neural network pruning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.988525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 30268781-cf54-4343-8e45-b5f1ece07eef · outbound

This paper cites Topology-aware network pruning using multi- stage graph embedding and reinforcement learn- ing.

Layer Pruning with Consensus: A Triple-Win Solution Topology-aware network pruning using multi- stage graph embedding and reinforcement learn- ing

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.938646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1418fe26-b6d9-4efe-a105-5a5264fbf1ee · outbound

This paper cites Are all layers created equal? JMLR, 2022.

Layer Pruning with Consensus: A Triple-Win Solution Are all layers created equal? JMLR, 2022

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.871372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 65a9045f-a5c1-412b-8121-f2ff475352a6 · outbound

This paper cites Layer pruning for obtaining shallower resnets.

Layer Pruning with Consensus: A Triple-Win Solution Layer pruning for obtaining shallower resnets

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.829317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fe34db74-df5f-449d-a086-2fe9910087d3 · outbound

This paper cites Carrying out CNN channel prun- ing in a white box.

Layer Pruning with Consensus: A Triple-Win Solution Carrying out CNN channel prun- ing in a white box

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.764539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 076573ce-6440-4536-aae0-b2a87d484c67 · outbound

This paper cites Revisit kernel pruning with lottery regulated grouped convolutions.

Layer Pruning with Consensus: A Triple-Win Solution Revisit kernel pruning with lottery regulated grouped convolutions

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.724580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 93ded09e-ff23-4bee-bfd3-342700372892 · outbound

This paper cites Learning N: M fine-grained structured sparse neural networks from scratch.

Layer Pruning with Consensus: A Triple-Win Solution Learning N: M fine-grained structured sparse neural networks from scratch

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.653228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2f36c62c-e22c-4662-8683-d19f76e3484a · outbound

This paper cites Yen, and Zhang Yi.

Layer Pruning with Consensus: A Triple-Win Solution Yen, and Zhang Yi

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:19:48.607941Z

Source-reported events for the cited work

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