Efficient Global Neural Architecture Search | Paper Citation Record

Observation 2656ea99-a1eb-41fc-bd53-91ccc1884098 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Efficient Global Neural Architecture Search Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 1

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Observation b519b9e6-f2cc-4310-a25e-b4875c7140f8 · outbound

This paper cites ”Fully convolutional networks for semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition.

Efficient Global Neural Architecture Search ”Fully convolutional networks for semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 2

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Observation 4b76dcfa-3500-4663-9e00-1e89b37381b9 · outbound

This paper cites ”Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition.

Efficient Global Neural Architecture Search ”Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 3

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Observation 8c32d690-e780-44c6-a813-02e1b40f8947 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Efficient Global Neural Architecture Search Neural Architecture Search with Reinforcement Learning

Reference 4

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This paper cites Designing Neural Network Architectures using Reinforcement Learning.

Efficient Global Neural Architecture Search Designing Neural Network Architectures using Reinforcement Learning

Reference 5

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Observation 9eaece77-de35-4c3a-834e-f5192e1c5586 · outbound

This paper cites ”Large-scale evolution of image classifiers.” International Con- ference on Machine Learning.

Efficient Global Neural Architecture Search ”Large-scale evolution of image classifiers.” International Con- ference on Machine Learning

Reference 6

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Observation 5f3099b6-78c6-4bcb-a7ce-b2794ca3a958 · outbound

This paper cites ”A genetic pro- gramming approach to designing convolutional neural network architectures.” Pro- ceedings of the genetic and evolutionary computation conference.

Efficient Global Neural Architecture Search ”A genetic pro- gramming approach to designing convolutional neural network architectures.” Pro- ceedings of the genetic and evolutionary computation conference

Reference 7

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Observation 7e64af3a-5665-4e18-aa3d-6fdff010735a · outbound

This paper cites Simple And Efficient Architecture Search for Convolutional Neural Networks.

Efficient Global Neural Architecture Search Simple And Efficient Architecture Search for Convolutional Neural Networks

Reference 8

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Observation 95d433ff-1ea4-41fa-9e8c-a634f12cee0c · outbound

This paper cites ”Learning transferable architectures for scalable image recogni- tion.” Proceedings of the IEEE conference on computer vision and pattern recogni- tion.

Efficient Global Neural Architecture Search ”Learning transferable architectures for scalable image recogni- tion.” Proceedings of the IEEE conference on computer vision and pattern recogni- tion

Reference 9

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Observation 824be423-7003-4b62-b430-01d7d53ccc1a · outbound

This paper cites ”Efficient architecture search by network transformation.” Pro- ceedings of the AAAI Conference on Artificial Intelligence.

Efficient Global Neural Architecture Search ”Efficient architecture search by network transformation.” Pro- ceedings of the AAAI Conference on Artificial Intelligence

Reference 10

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Observation ef2bb440-8a7d-43e1-b66c-d3757ddb4230 · outbound

This paper cites ”Neural architecture search with bayesian optimi- sation and optimal transport.” Advances in neural information processing systems 31 (2018).

Efficient Global Neural Architecture Search ”Neural architecture search with bayesian optimi- sation and optimal transport.” Advances in neural information processing systems 31 (2018)

Reference 11

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Observation 47d90052-ffa1-431b-b674-35e76a24dee4 · outbound

This paper cites ”Dpp-net: Device-aware progressive search for pareto- optimal neural architectures.” Proceedings of the European Conference on Com- puter Vision (ECCV).

Efficient Global Neural Architecture Search ”Dpp-net: Device-aware progressive search for pareto- optimal neural architectures.” Proceedings of the European Conference on Com- puter Vision (ECCV)

Reference 12

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Observation dbd0d65a-bfa4-47de-9f7b-38fb13c510e6 · outbound

This paper cites ”Macro neural architecture search revisited.” 2nd Workshop on Meta-Learning at NeurIPS.

Efficient Global Neural Architecture Search ”Macro neural architecture search revisited.” 2nd Workshop on Meta-Learning at NeurIPS

Reference 13

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Observation 35cae499-a7b8-4d95-a97a-4682199cd634 · outbound

This paper cites Efficient neural architecture search via parameters sharing.

Efficient Global Neural Architecture Search Efficient neural architecture search via parameters sharing

Reference 14

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Observation c0771a65-3c5a-41c4-a2fd-4a56ddfdb703 · outbound

This paper cites DARTS: Differentiable Architecture Search.

Efficient Global Neural Architecture Search DARTS: Differentiable Architecture Search

Reference 15

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This paper cites ”Searching for a robust neural architecture in four gpu hours.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Efficient Global Neural Architecture Search ”Searching for a robust neural architecture in four gpu hours.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 16

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Observation d654e5ed-742d-4911-8561-42cc278dd07f · outbound

This paper cites ”Efficient forward architecture search.” Advances in Neural Information Processing Systems 32 (2019).

Efficient Global Neural Architecture Search ”Efficient forward architecture search.” Advances in Neural Information Processing Systems 32 (2019)

Reference 17

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This paper cites NAS evaluation is frustratingly hard.

Efficient Global Neural Architecture Search NAS evaluation is frustratingly hard

Reference 18

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Observation 32c226ca-ab59-4904-a7c0-55232999696a · outbound

This paper cites ”Nsga-net: neural architecture search using multi-objective ge- netic algorithm.” Proceedings of the genetic and evolutionary computation confer- ence.

Efficient Global Neural Architecture Search ”Nsga-net: neural architecture search using multi-objective ge- netic algorithm.” Proceedings of the genetic and evolutionary computation confer- ence

Reference 19

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Observation 004bf2eb-a62e-4865-bc09-bddf9830b94b · outbound

This paper cites ”Best practices for scientific research on neu- ral architecture search.” The Journal of Machine Learning Research 21.1 (2020): 9820-9837.

Efficient Global Neural Architecture Search ”Best practices for scientific research on neu- ral architecture search.” The Journal of Machine Learning Research 21.1 (2020): 9820-9837

Reference 20

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Observation e85e9fff-797e-4f7f-a65c-5828ff8c842d · outbound

This paper cites ”A comprehensive survey of neural architecture search: Chal- lenges and solutions.” ACM Computing Surveys (CSUR) 54.4 (2021): 1-34.

Efficient Global Neural Architecture Search ”A comprehensive survey of neural architecture search: Chal- lenges and solutions.” ACM Computing Surveys (CSUR) 54.4 (2021): 1-34

Reference 21

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Observation 6d61a473-b8c3-475f-a08d-af0c212e2363 · outbound

This paper cites ”How powerful are performance predictors in neural archi- tecture search?.” Advances in Neural Information Processing Systems 34 (2021): 28454-28469.

Efficient Global Neural Architecture Search ”How powerful are performance predictors in neural archi- tecture search?.” Advances in Neural Information Processing Systems 34 (2021): 28454-28469

Reference 22

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Observation 5215e30c-0b7e-4309-8daf-9830d01bee84 · outbound

This paper cites ”AGNAS: Attention-Guided Micro and Macro-Architecture Search.” International Conference on Machine Learning.

Efficient Global Neural Architecture Search ”AGNAS: Attention-Guided Micro and Macro-Architecture Search.” International Conference on Machine Learning

Reference 23

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Observation 7bcbd4e8-938f-4962-96fe-daa7b5d9afa3 · outbound

This paper cites Towards Less Constrained Macro-Neural Architecture Search.

Efficient Global Neural Architecture Search Towards Less Constrained Macro-Neural Architecture Search

Reference 24

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Observation 9482e861-4c3a-452b-84e3-bf3e627d11eb · outbound

This paper cites ”Neural architecture search using progres- sive evolution.” Proceedings of the Genetic and Evolutionary Computation Confer- ence.

Efficient Global Neural Architecture Search ”Neural architecture search using progres- sive evolution.” Proceedings of the Genetic and Evolutionary Computation Confer- ence

Reference 25

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Observation 21af4ccd-c4ff-4685-9faa-7a0614c81413 · outbound

This paper cites Evaluating the search phase of neural architecture search.

Efficient Global Neural Architecture Search Evaluating the search phase of neural architecture search

Reference 26

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Observation 8da3c183-b5e9-4357-bc8a-2bdb4177b1e1 · outbound

This paper cites Neural Architecture Search: Insights from 1000 Papers.

Efficient Global Neural Architecture Search Neural Architecture Search: Insights from 1000 Papers

Reference 27

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This paper cites Jahs-bench-201: A foundation for research on joint architecture and hyperparameter search.

Efficient Global Neural Architecture Search Jahs-bench-201: A foundation for research on joint architecture and hyperparameter search

Reference 28

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Observation 089ecf3e-d58b-47f6-a81c-aecd72c739ad · outbound

This paper cites Accelerating Neural Architecture Search using Performance Prediction.

Efficient Global Neural Architecture Search Accelerating Neural Architecture Search using Performance Prediction

Reference 29

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Efficient Global Neural Architecture Search Unresolved cited work

Reference 30

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Observation d182e756-c476-4e50-9cdd-9d72ca7978f6 · outbound

This paper cites Neural Predictor for Neural Architecture Search.

Efficient Global Neural Architecture Search Neural Predictor for Neural Architecture Search

Reference 31

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Observation b46d4844-1aca-40ec-b2c2-ee7ce7eb3f9e · outbound

This paper cites NAS-Bench-Suite- Zero: Accelerating research on zero cost proxies.

Efficient Global Neural Architecture Search NAS-Bench-Suite- Zero: Accelerating research on zero cost proxies

Reference 32

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This paper cites Nas-bench-zero: A large scale dataset for understanding zero-shot neural architecture search.

Efficient Global Neural Architecture Search Nas-bench-zero: A large scale dataset for understanding zero-shot neural architecture search

Reference 33

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Observation 1e5ca1b8-40a0-46c9-bfdf-54804451ebb9 · outbound

This paper cites Evaluating efficient performance estimators of neu- ral architectures.

Efficient Global Neural Architecture Search Evaluating efficient performance estimators of neu- ral architectures

Reference 34

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This paper cites an unresolved cited work.

Efficient Global Neural Architecture Search Unresolved cited work

Reference 35

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Observation 0102ca29-e8b4-4d2e-a4d3-2a17d98654b8 · outbound

This paper cites WaveMix: A Resource-efficient Neural Network for Image Analysis.

Efficient Global Neural Architecture Search WaveMix: A Resource-efficient Neural Network for Image Analysis

Reference 36

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Observation cd483f3d-6635-4702-859d-38c3c7c92e52 · outbound

This paper cites NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search.

Efficient Global Neural Architecture Search NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Reference 37

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Efficient Global Neural Architecture Search Unresolved cited work

Reference 38

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This paper cites An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems.

Efficient Global Neural Architecture Search An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems

Reference 39

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Efficient Global Neural Architecture Search Unresolved cited work

Reference 40

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Efficient Global Neural Architecture Search Unresolved cited work

Reference 41

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Observation d34c312c-ad0e-462a-8147-684f08fd2d2a · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Efficient Global Neural Architecture Search Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 42

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This paper cites EMNIST: Extending MNIST to hand- written letters.

Efficient Global Neural Architecture Search EMNIST: Extending MNIST to hand- written letters

Reference 43

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This paper cites Deep Learning for Classical Japanese Literature.

Efficient Global Neural Architecture Search Deep Learning for Classical Japanese Literature

Reference 44

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This paper cites Imagenet large scale visual recognition challenge.

Efficient Global Neural Architecture Search Imagenet large scale visual recognition challenge

Reference 45

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Observation 1d28e4dc-2976-4f00-a9ba-5159c664c053 · outbound

This paper cites Sgas: Sequential greedy architecture search.

Efficient Global Neural Architecture Search Sgas: Sequential greedy architecture search

Reference 46

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Efficient Global Neural Architecture Search Unresolved cited work

Reference 47

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Observation 860b1583-0a19-4625-b97a-56b7211945ab · outbound

This paper cites Learning Face Representation from Scratch.

Efficient Global Neural Architecture Search Learning Face Representation from Scratch

Reference 48

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Observation fef62ce3-eaaa-4b3b-aea9-1d214c4a28ba · outbound

This paper cites Labeled faces in the wild: A database forstudying face recognition in unconstrained environments.

Efficient Global Neural Architecture Search Labeled faces in the wild: A database forstudying face recognition in unconstrained environments

Reference 49

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Observation 35d7aad5-e9be-4913-ac93-8d1863f97b83 · outbound

This paper cites Frontal to profile face verification in the wild.

Efficient Global Neural Architecture Search Frontal to profile face verification in the wild

Reference 50

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Observation 529960bf-af5e-4267-b8f5-6e4d943365ac · outbound

This paper cites Cross-pose lfw: A database for studying cross-pose face recog- nition in unconstrained environments.

Efficient Global Neural Architecture Search Cross-pose lfw: A database for studying cross-pose face recog- nition in unconstrained environments

Reference 51

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Observation 2e586018-e2da-402f-9122-dcb2162e6f48 · outbound

This paper cites Agedb: the first manually collected, in-the-wild age database.

Efficient Global Neural Architecture Search Agedb: the first manually collected, in-the-wild age database

Reference 52

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Observation 61166ba1-9d8b-4558-9516-7c902ef3374f · outbound

This paper cites Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments.

Efficient Global Neural Architecture Search Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

Reference 53

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This paper cites Iarpa janus benchmark-b face dataset.

Efficient Global Neural Architecture Search Iarpa janus benchmark-b face dataset

Reference 54

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Observation d855b281-0aa2-4a0c-a900-bf75ca07271a · outbound

This paper cites Iarpa janus benchmark-c: Face dataset and proto- col.

Efficient Global Neural Architecture Search Iarpa janus benchmark-c: Face dataset and proto- col

Reference 55

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Observation 40aac6ad-587d-4cf2-9ba8-7171887be1fc · outbound

This paper cites Low-resolution face recognition.

Efficient Global Neural Architecture Search Low-resolution face recognition

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:33:54.561590Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08dbc3eb-cf43-434c-9bfb-93ab2adb6639 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition.

Efficient Global Neural Architecture Search Arcface: Additive angular margin loss for deep face recognition

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:33:54.549363Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7914f024-408f-48c2-9403-8d03ac803d5a · outbound

This paper cites Adaface: Quality adaptive margin for face recognition.

Efficient Global Neural Architecture Search Adaface: Quality adaptive margin for face recognition

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:33:54.537992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8432c62-6594-417a-8867-440bd415eead · outbound

This paper cites Rethinking bias mitigation: Fairer architectures make for fairer face recognition.

Efficient Global Neural Architecture Search Rethinking bias mitigation: Fairer architectures make for fairer face recognition

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:33:54.528042Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 423f9a98-3dab-4087-8cc3-e48a6d8295a7 · outbound

This paper cites Teacher guided neural architecture search for face recognition.

Efficient Global Neural Architecture Search Teacher guided neural architecture search for face recognition

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:33:54.518352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e119ffc-f09d-4cf3-82a2-6167372f1cee · outbound

This paper cites Pocketnet: Extreme lightweight face recognition network using neural architecture search and multistep knowledge distillation.

Efficient Global Neural Architecture Search Pocketnet: Extreme lightweight face recognition network using neural architecture search and multistep knowledge distillation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:33:54.508610Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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