DAP: A dataset-agnostic predictor of neural network performance
<p>Training a deep neural network on a large dataset to convergence is a time-demanding task. This task often must be repeated many times, especially when developing a new deep learning algorithm or performing a neural architecture search. This problem can be mitigated if a deep neural network...
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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| _version_ | 1864513509806571520 |
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| author | Sui Paul Ang (18460605) |
| author2 | Soan T.M. Duong (19256503) Son Lam Phung (18460602) Abdesselam Bouzerdoum (17900021) |
| author2_role | author author author |
| author_facet | Sui Paul Ang (18460605) Soan T.M. Duong (19256503) Son Lam Phung (18460602) Abdesselam Bouzerdoum (17900021) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sui Paul Ang (18460605) Soan T.M. Duong (19256503) Son Lam Phung (18460602) Abdesselam Bouzerdoum (17900021) |
| dc.date.none.fl_str_mv | 2024-03-18T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.neucom.2024.127544 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/DAP_A_dataset-agnostic_predictor_of_neural_network_performance/26404033 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Neural network performance predictor Deep learning Dataset-agnostic Neural architecture search AutoML |
| dc.title.none.fl_str_mv | DAP: A dataset-agnostic predictor of neural network performance |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Training a deep neural network on a large dataset to convergence is a time-demanding task. This task often must be repeated many times, especially when developing a new deep learning algorithm or performing a neural architecture search. This problem can be mitigated if a deep neural network’s performance can be estimated without actually training it. In this work, we investigate the feasibility of two tasks: (i) predicting a deep neural network’s performance accurately given only its architectural descriptor, and (ii) generalizing the predictor across different datasets without re-training. To this end, we propose a dataset-agnostic regression framework that uses a novel dual-LSTM model and a new dataset difficulty feature. The experimental results show that both tasks above are indeed feasible, and the proposed method outperforms the existing techniques in all experimental cases. Additionally, we also demonstrate several practical use-cases of the proposed predictor.</p><h2>Other Information</h2> <p> Published in: Neurocomputing<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.neucom.2024.127544" target="_blank">https://dx.doi.org/10.1016/j.neucom.2024.127544</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a6fe8238ed8c29a37b21730be0a029df |
| identifier_str_mv | 10.1016/j.neucom.2024.127544 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26404033 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | DAP: A dataset-agnostic predictor of neural network performanceSui Paul Ang (18460605)Soan T.M. Duong (19256503)Son Lam Phung (18460602)Abdesselam Bouzerdoum (17900021)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningNeural network performance predictorDeep learningDataset-agnosticNeural architecture searchAutoML<p>Training a deep neural network on a large dataset to convergence is a time-demanding task. This task often must be repeated many times, especially when developing a new deep learning algorithm or performing a neural architecture search. This problem can be mitigated if a deep neural network’s performance can be estimated without actually training it. In this work, we investigate the feasibility of two tasks: (i) predicting a deep neural network’s performance accurately given only its architectural descriptor, and (ii) generalizing the predictor across different datasets without re-training. To this end, we propose a dataset-agnostic regression framework that uses a novel dual-LSTM model and a new dataset difficulty feature. The experimental results show that both tasks above are indeed feasible, and the proposed method outperforms the existing techniques in all experimental cases. Additionally, we also demonstrate several practical use-cases of the proposed predictor.</p><h2>Other Information</h2> <p> Published in: Neurocomputing<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.neucom.2024.127544" target="_blank">https://dx.doi.org/10.1016/j.neucom.2024.127544</a></p>2024-03-18T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.neucom.2024.127544https://figshare.com/articles/journal_contribution/DAP_A_dataset-agnostic_predictor_of_neural_network_performance/26404033CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264040332024-03-18T09:00:00Z |
| spellingShingle | DAP: A dataset-agnostic predictor of neural network performance Sui Paul Ang (18460605) Information and computing sciences Artificial intelligence Data management and data science Machine learning Neural network performance predictor Deep learning Dataset-agnostic Neural architecture search AutoML |
| status_str | publishedVersion |
| title | DAP: A dataset-agnostic predictor of neural network performance |
| title_full | DAP: A dataset-agnostic predictor of neural network performance |
| title_fullStr | DAP: A dataset-agnostic predictor of neural network performance |
| title_full_unstemmed | DAP: A dataset-agnostic predictor of neural network performance |
| title_short | DAP: A dataset-agnostic predictor of neural network performance |
| title_sort | DAP: A dataset-agnostic predictor of neural network performance |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Neural network performance predictor Deep learning Dataset-agnostic Neural architecture search AutoML |