Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup Article Swipe
Maolin Wang
,
Yao Zhao
,
Jiajia Liu
,
Jingdong Chen
,
Chenyi Zhuang
,
Jinjie Gu
,
Ruocheng Guo
,
Xiangyu Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.05795
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.05795
The deployment of Large Multimodal Models (LMMs) within AntGroup has significantly advanced multimodal tasks in payment, security, and advertising, notably enhancing advertisement audition tasks in Alipay. However, the deployment of such sizable models introduces challenges, particularly in increased latency and carbon emissions, which are antithetical to the ideals of Green AI. This paper introduces a novel multi-stage compression strategy for our proprietary LLM, AntGMM. Our methodology pivots on three main aspects: employing small training sample sizes, addressing multi-level redundancy through multi-stage pruning, and introducing an advanced distillation loss design. In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy. Furthermore, the effectiveness of our strategy is evident in its operational success in Alipay's real-world multimodal advertisement audition for three months from September 2023. Notably, our approach achieved a substantial reduction in latency, decreasing it from 700ms to 90ms, while maintaining online performance with only a slight performance decrease. Moreover, our compressed model is estimated to reduce electricity consumption by approximately 75 million kWh annually compared to the direct deployment of AntGMM, demonstrating our commitment to green AI initiatives. We will publicly release our code and the MAAD dataset after some reviews\footnote{https://github.com/MorinW/AntGMM$\_$Pruning}.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.05795
- https://arxiv.org/pdf/2312.05795
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389650658
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389650658Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2312.05795Digital Object Identifier
- Title
-
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroupWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-10Full publication date if available
- Authors
-
Maolin Wang, Yao Zhao, Jiajia Liu, Jingdong Chen, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu ZhaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.05795Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.05795Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.05795Direct OA link when available
- Concepts
-
Computer science, Software deployment, Pruning, Machine learning, Redundancy (engineering), Artificial intelligence, Software engineering, Biology, Operating system, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4389650658 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2312.05795 |
| ids.doi | https://doi.org/10.48550/arxiv.2312.05795 |
| ids.openalex | https://openalex.org/W4389650658 |
| fwci | |
| type | preprint |
| title | Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11439 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9702000021934509 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Video Analysis and Summarization |
| topics[1].id | https://openalex.org/T10028 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9650999903678894 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Topic Modeling |
| topics[2].id | https://openalex.org/T11714 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9413999915122986 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Multimodal Machine Learning Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7570889592170715 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C105339364 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7293151617050171 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2297740 |
| concepts[1].display_name | Software deployment |
| concepts[2].id | https://openalex.org/C108010975 |
| concepts[2].level | 2 |
| concepts[2].score | 0.4753258526325226 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q500094 |
| concepts[2].display_name | Pruning |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.47488516569137573 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C152124472 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4200893044471741 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1204361 |
| concepts[4].display_name | Redundancy (engineering) |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4040352404117584 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C115903868 |
| concepts[6].level | 1 |
| concepts[6].score | 0.14043787121772766 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q80993 |
| concepts[6].display_name | Software engineering |
| concepts[7].id | https://openalex.org/C86803240 |
| concepts[7].level | 0 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[7].display_name | Biology |
| concepts[8].id | https://openalex.org/C111919701 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[8].display_name | Operating system |
| concepts[9].id | https://openalex.org/C6557445 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q173113 |
| concepts[9].display_name | Agronomy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7570889592170715 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/software-deployment |
| keywords[1].score | 0.7293151617050171 |
| keywords[1].display_name | Software deployment |
| keywords[2].id | https://openalex.org/keywords/pruning |
| keywords[2].score | 0.4753258526325226 |
| keywords[2].display_name | Pruning |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.47488516569137573 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/redundancy |
| keywords[4].score | 0.4200893044471741 |
| keywords[4].display_name | Redundancy (engineering) |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4040352404117584 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/software-engineering |
| keywords[6].score | 0.14043787121772766 |
| keywords[6].display_name | Software engineering |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2312.05795 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2312.05795 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2312.05795 |
| locations[1].id | doi:10.48550/arxiv.2312.05795 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2312.05795 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5014749523 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7449-9834 |
| authorships[0].author.display_name | Maolin Wang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wang, Maolin |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5100362745 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8581-9554 |
| authorships[1].author.display_name | Yao Zhao |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhao, Yao |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5108050448 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4273-8866 |
| authorships[2].author.display_name | Jiajia Liu |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Liu, Jiajia |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5056129529 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-0083-9247 |
| authorships[3].author.display_name | Jingdong Chen |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Chen, Jingdong |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5061810614 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-1891-5666 |
| authorships[4].author.display_name | Chenyi Zhuang |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Zhuang, Chenyi |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5053242349 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-7596-4945 |
| authorships[5].author.display_name | Jinjie Gu |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Gu, Jinjie |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5054719216 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-8522-6142 |
| authorships[6].author.display_name | Ruocheng Guo |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Guo, Ruocheng |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5100645854 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-2926-4416 |
| authorships[7].author.display_name | Xiangyu Zhao |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Zhao, Xiangyu |
| authorships[7].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2312.05795 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-12-13T00:00:00 |
| display_name | Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11439 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9702000021934509 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Video Analysis and Summarization |
| related_works | https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W3046775127, https://openalex.org/W3107602296, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W4364306694, https://openalex.org/W4312192474, https://openalex.org/W4283697347, https://openalex.org/W4210805261 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2312.05795 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2312.05795 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2312.05795 |
| primary_location.id | pmh:oai:arXiv.org:2312.05795 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2312.05795 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2312.05795 |
| publication_date | 2023-12-10 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 54, 94, 146, 163 |
| abstract_inverted_index.75 | 179 |
| abstract_inverted_index.AI | 195 |
| abstract_inverted_index.In | 89 |
| abstract_inverted_index.We | 197 |
| abstract_inverted_index.an | 84 |
| abstract_inverted_index.by | 177 |
| abstract_inverted_index.in | 14, 24, 36, 126, 130, 149 |
| abstract_inverted_index.is | 124, 171 |
| abstract_inverted_index.it | 152 |
| abstract_inverted_index.of | 2, 29, 48, 114, 121, 188 |
| abstract_inverted_index.on | 67 |
| abstract_inverted_index.to | 45, 110, 155, 173, 184, 193 |
| abstract_inverted_index.we | 92 |
| abstract_inverted_index.AI. | 50 |
| abstract_inverted_index.Our | 64 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 17, 39, 82, 107, 203 |
| abstract_inverted_index.are | 43 |
| abstract_inverted_index.for | 59, 136 |
| abstract_inverted_index.has | 9 |
| abstract_inverted_index.its | 127 |
| abstract_inverted_index.kWh | 181 |
| abstract_inverted_index.our | 60, 90, 115, 122, 143, 168, 191, 201 |
| abstract_inverted_index.the | 27, 46, 96, 112, 119, 185, 204 |
| abstract_inverted_index.LLM, | 62 |
| abstract_inverted_index.MAAD | 205 |
| abstract_inverted_index.This | 51 |
| abstract_inverted_index.code | 202 |
| abstract_inverted_index.from | 102, 139, 153 |
| abstract_inverted_index.loss | 87 |
| abstract_inverted_index.main | 69 |
| abstract_inverted_index.only | 162 |
| abstract_inverted_index.some | 208 |
| abstract_inverted_index.such | 30 |
| abstract_inverted_index.will | 198 |
| abstract_inverted_index.with | 161 |
| abstract_inverted_index.2023. | 141 |
| abstract_inverted_index.700ms | 154 |
| abstract_inverted_index.90ms, | 156 |
| abstract_inverted_index.Green | 49 |
| abstract_inverted_index.Large | 3 |
| abstract_inverted_index.after | 207 |
| abstract_inverted_index.green | 194 |
| abstract_inverted_index.model | 170 |
| abstract_inverted_index.novel | 55 |
| abstract_inverted_index.paper | 52 |
| abstract_inverted_index.small | 72 |
| abstract_inverted_index.tasks | 13, 23 |
| abstract_inverted_index.three | 68, 137 |
| abstract_inverted_index.which | 42 |
| abstract_inverted_index.while | 157 |
| abstract_inverted_index.(LMMs) | 6 |
| abstract_inverted_index.Models | 5 |
| abstract_inverted_index.carbon | 40 |
| abstract_inverted_index.direct | 186 |
| abstract_inverted_index.ideals | 47 |
| abstract_inverted_index.models | 32 |
| abstract_inverted_index.months | 138 |
| abstract_inverted_index.online | 159 |
| abstract_inverted_index.pivots | 66 |
| abstract_inverted_index.reduce | 174 |
| abstract_inverted_index.sample | 74 |
| abstract_inverted_index.sizes, | 75 |
| abstract_inverted_index.slight | 164 |
| abstract_inverted_index.within | 7, 105 |
| abstract_inverted_index.(MAAD), | 101 |
| abstract_inverted_index.Alipay, | 106 |
| abstract_inverted_index.Alipay. | 25 |
| abstract_inverted_index.AntGMM, | 189 |
| abstract_inverted_index.AntGMM. | 63 |
| abstract_inverted_index.Dataset | 100 |
| abstract_inverted_index.dataset | 206 |
| abstract_inverted_index.design. | 88 |
| abstract_inverted_index.evident | 125 |
| abstract_inverted_index.latency | 38 |
| abstract_inverted_index.million | 180 |
| abstract_inverted_index.notably | 19 |
| abstract_inverted_index.release | 200 |
| abstract_inverted_index.sizable | 31 |
| abstract_inverted_index.success | 129 |
| abstract_inverted_index.through | 79 |
| abstract_inverted_index.Alipay's | 131 |
| abstract_inverted_index.AntGroup | 8 |
| abstract_inverted_index.Audition | 99 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.Notably, | 142 |
| abstract_inverted_index.achieved | 145 |
| abstract_inverted_index.advanced | 11, 85 |
| abstract_inverted_index.annually | 182 |
| abstract_inverted_index.approach | 144 |
| abstract_inverted_index.aspects: | 70 |
| abstract_inverted_index.audition | 22, 135 |
| abstract_inverted_index.compared | 183 |
| abstract_inverted_index.dataset, | 95 |
| abstract_inverted_index.latency, | 150 |
| abstract_inverted_index.payment, | 15 |
| abstract_inverted_index.proposed | 116 |
| abstract_inverted_index.pruning, | 81 |
| abstract_inverted_index.publicly | 199 |
| abstract_inverted_index.strategy | 58, 123 |
| abstract_inverted_index.training | 73 |
| abstract_inverted_index.validate | 111 |
| abstract_inverted_index.Moreover, | 167 |
| abstract_inverted_index.September | 140 |
| abstract_inverted_index.conducted | 108 |
| abstract_inverted_index.decrease. | 166 |
| abstract_inverted_index.employing | 71 |
| abstract_inverted_index.enhancing | 20 |
| abstract_inverted_index.estimated | 172 |
| abstract_inverted_index.increased | 37 |
| abstract_inverted_index.reduction | 148 |
| abstract_inverted_index.research, | 91 |
| abstract_inverted_index.scenarios | 104 |
| abstract_inverted_index.security, | 16 |
| abstract_inverted_index.strategy. | 117 |
| abstract_inverted_index.Multimodal | 4, 97 |
| abstract_inverted_index.addressing | 76 |
| abstract_inverted_index.commitment | 192 |
| abstract_inverted_index.compressed | 169 |
| abstract_inverted_index.decreasing | 151 |
| abstract_inverted_index.deployment | 1, 28, 187 |
| abstract_inverted_index.emissions, | 41 |
| abstract_inverted_index.introduces | 33, 53 |
| abstract_inverted_index.multimodal | 12, 133 |
| abstract_inverted_index.real-world | 103, 132 |
| abstract_inverted_index.redundancy | 78 |
| abstract_inverted_index.challenges, | 34 |
| abstract_inverted_index.compression | 57 |
| abstract_inverted_index.constructed | 93 |
| abstract_inverted_index.consumption | 176 |
| abstract_inverted_index.electricity | 175 |
| abstract_inverted_index.experiments | 109 |
| abstract_inverted_index.introducing | 83 |
| abstract_inverted_index.maintaining | 158 |
| abstract_inverted_index.methodology | 65 |
| abstract_inverted_index.multi-level | 77 |
| abstract_inverted_index.multi-stage | 56, 80 |
| abstract_inverted_index.operational | 128 |
| abstract_inverted_index.performance | 160, 165 |
| abstract_inverted_index.proprietary | 61 |
| abstract_inverted_index.reliability | 113 |
| abstract_inverted_index.substantial | 147 |
| abstract_inverted_index.Furthermore, | 118 |
| abstract_inverted_index.advertising, | 18 |
| abstract_inverted_index.antithetical | 44 |
| abstract_inverted_index.distillation | 86 |
| abstract_inverted_index.initiatives. | 196 |
| abstract_inverted_index.particularly | 35 |
| abstract_inverted_index.Advertisement | 98 |
| abstract_inverted_index.advertisement | 21, 134 |
| abstract_inverted_index.approximately | 178 |
| abstract_inverted_index.demonstrating | 190 |
| abstract_inverted_index.effectiveness | 120 |
| abstract_inverted_index.significantly | 10 |
| abstract_inverted_index.reviews\footnote{https://github.com/MorinW/AntGMM$\_$Pruning}. | 209 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |