Context-Aware Meta-Learning Article Swipe
Christopher Fifty
,
D. Duan
,
Ronald G. Junkins
,
Ehsan Amid
,
Jure Leskovec
,
Christopher Ré
,
Sebastian Thrun
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.10971
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.10971
Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable to replicate this ability, and instead either perform poorly or require meta-training and/or fine-tuning on similar objects. In this work, we propose a meta-learning algorithm that emulates Large Language Models by learning new visual concepts during inference without fine-tuning. Our approach leverages a frozen pre-trained feature extractor, and analogous to in-context learning, recasts visual meta-learning as sequence modeling over datapoints with known labels and a test datapoint with an unknown label. On 8 out of 11 meta-learning benchmarks, our approach -- without meta-training or fine-tuning -- exceeds or matches the state-of-the-art algorithm, P>M>F, which is meta-trained on these benchmarks. Our code is available at https://github.com/cfifty/CAML.
Related Topics
Concepts
Computer science
Inference
Meta learning (computer science)
Artificial intelligence
Machine learning
Context (archaeology)
Replicate
Feature (linguistics)
Structured prediction
Code (set theory)
Extractor
Natural language processing
Programming language
Biology
Mathematics
Task (project management)
Paleontology
Statistics
Engineering
Linguistics
Set (abstract data type)
Management
Process engineering
Philosophy
Economics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.10971
- https://arxiv.org/pdf/2310.10971
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387797197
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387797197Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2310.10971Digital Object Identifier
- Title
-
Context-Aware Meta-LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-17Full publication date if available
- Authors
-
Christopher Fifty, D. Duan, Ronald G. Junkins, Ehsan Amid, Jure Leskovec, Christopher Ré, Sebastian ThrunList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.10971Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.10971Direct 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/2310.10971Direct OA link when available
- Concepts
-
Computer science, Inference, Meta learning (computer science), Artificial intelligence, Machine learning, Context (archaeology), Replicate, Feature (linguistics), Structured prediction, Code (set theory), Extractor, Natural language processing, Programming language, Biology, Mathematics, Task (project management), Paleontology, Statistics, Engineering, Linguistics, Set (abstract data type), Management, Process engineering, Philosophy, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387797197 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2310.10971 |
| ids.doi | https://doi.org/10.48550/arxiv.2310.10971 |
| ids.openalex | https://openalex.org/W4387797197 |
| fwci | |
| type | preprint |
| title | Context-Aware Meta-Learning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11714 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9976000189781189 |
| 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 | Multimodal Machine Learning Applications |
| topics[1].id | https://openalex.org/T11307 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9943000078201294 |
| 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 | Domain Adaptation and Few-Shot Learning |
| topics[2].id | https://openalex.org/T10028 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9919000267982483 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Topic Modeling |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8250521421432495 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2776214188 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7920594215393066 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[1].display_name | Inference |
| concepts[2].id | https://openalex.org/C2781002164 |
| concepts[2].level | 3 |
| concepts[2].score | 0.7166843414306641 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q6822311 |
| concepts[2].display_name | Meta learning (computer science) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.639138400554657 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.601123034954071 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C2779343474 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5831387042999268 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[5].display_name | Context (archaeology) |
| concepts[6].id | https://openalex.org/C2781162219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5569124221801758 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q26250693 |
| concepts[6].display_name | Replicate |
| concepts[7].id | https://openalex.org/C2776401178 |
| concepts[7].level | 2 |
| concepts[7].score | 0.533488392829895 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[7].display_name | Feature (linguistics) |
| concepts[8].id | https://openalex.org/C22367795 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5267897248268127 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7625208 |
| concepts[8].display_name | Structured prediction |
| concepts[9].id | https://openalex.org/C2776760102 |
| concepts[9].level | 3 |
| concepts[9].score | 0.4943430423736572 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q5139990 |
| concepts[9].display_name | Code (set theory) |
| concepts[10].id | https://openalex.org/C117978034 |
| concepts[10].level | 2 |
| concepts[10].score | 0.41643089056015015 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q5422192 |
| concepts[10].display_name | Extractor |
| concepts[11].id | https://openalex.org/C204321447 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3664042055606842 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[11].display_name | Natural language processing |
| concepts[12].id | https://openalex.org/C199360897 |
| concepts[12].level | 1 |
| concepts[12].score | 0.163292795419693 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[12].display_name | Programming language |
| concepts[13].id | https://openalex.org/C86803240 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[13].display_name | Biology |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C2780451532 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[15].display_name | Task (project management) |
| concepts[16].id | https://openalex.org/C151730666 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[16].display_name | Paleontology |
| concepts[17].id | https://openalex.org/C105795698 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[17].display_name | Statistics |
| concepts[18].id | https://openalex.org/C127413603 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[18].display_name | Engineering |
| concepts[19].id | https://openalex.org/C41895202 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[19].display_name | Linguistics |
| concepts[20].id | https://openalex.org/C177264268 |
| concepts[20].level | 2 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[20].display_name | Set (abstract data type) |
| concepts[21].id | https://openalex.org/C187736073 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[21].display_name | Management |
| concepts[22].id | https://openalex.org/C21880701 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q2144042 |
| concepts[22].display_name | Process engineering |
| concepts[23].id | https://openalex.org/C138885662 |
| concepts[23].level | 0 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[23].display_name | Philosophy |
| concepts[24].id | https://openalex.org/C162324750 |
| concepts[24].level | 0 |
| concepts[24].score | 0.0 |
| concepts[24].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[24].display_name | Economics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8250521421432495 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/inference |
| keywords[1].score | 0.7920594215393066 |
| keywords[1].display_name | Inference |
| keywords[2].id | https://openalex.org/keywords/meta-learning |
| keywords[2].score | 0.7166843414306641 |
| keywords[2].display_name | Meta learning (computer science) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.639138400554657 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.601123034954071 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/context |
| keywords[5].score | 0.5831387042999268 |
| keywords[5].display_name | Context (archaeology) |
| keywords[6].id | https://openalex.org/keywords/replicate |
| keywords[6].score | 0.5569124221801758 |
| keywords[6].display_name | Replicate |
| keywords[7].id | https://openalex.org/keywords/feature |
| keywords[7].score | 0.533488392829895 |
| keywords[7].display_name | Feature (linguistics) |
| keywords[8].id | https://openalex.org/keywords/structured-prediction |
| keywords[8].score | 0.5267897248268127 |
| keywords[8].display_name | Structured prediction |
| keywords[9].id | https://openalex.org/keywords/code |
| keywords[9].score | 0.4943430423736572 |
| keywords[9].display_name | Code (set theory) |
| keywords[10].id | https://openalex.org/keywords/extractor |
| keywords[10].score | 0.41643089056015015 |
| keywords[10].display_name | Extractor |
| keywords[11].id | https://openalex.org/keywords/natural-language-processing |
| keywords[11].score | 0.3664042055606842 |
| keywords[11].display_name | Natural language processing |
| keywords[12].id | https://openalex.org/keywords/programming-language |
| keywords[12].score | 0.163292795419693 |
| keywords[12].display_name | Programming language |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2310.10971 |
| 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/2310.10971 |
| 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/2310.10971 |
| locations[1].id | doi:10.48550/arxiv.2310.10971 |
| 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.2310.10971 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5021315343 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Christopher Fifty |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Fifty, Christopher |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5087680058 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6304-9448 |
| authorships[1].author.display_name | D. Duan |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Duan, Dennis |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5093096213 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Ronald G. Junkins |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Junkins, Ronald G. |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5056776503 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6097-0226 |
| authorships[3].author.display_name | Ehsan Amid |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Amid, Ehsan |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5091272738 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5411-923X |
| authorships[4].author.display_name | Jure Leskovec |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Leskovec, Jure |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5103852640 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Christopher Ré |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Ré, Christopher |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5066710774 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Sebastian Thrun |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Thrun, Sebastian |
| authorships[6].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/2310.10971 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Context-Aware Meta-Learning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11714 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9976000189781189 |
| 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 | Multimodal Machine Learning Applications |
| related_works | https://openalex.org/W2785442381, https://openalex.org/W2189749715, https://openalex.org/W3008227517, https://openalex.org/W3103344181, https://openalex.org/W2987799900, https://openalex.org/W42646284, https://openalex.org/W2770224089, https://openalex.org/W2070797946, https://openalex.org/W2752328056, https://openalex.org/W3034718129 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2310.10971 |
| 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/2310.10971 |
| 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/2310.10971 |
| primary_location.id | pmh:oai:arXiv.org:2310.10971 |
| 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/2310.10971 |
| 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/2310.10971 |
| publication_date | 2023-10-17 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.8 | 103 |
| abstract_inverted_index.a | 6, 53, 73, 95 |
| abstract_inverted_index.-- | 111, 116 |
| abstract_inverted_index.11 | 106 |
| abstract_inverted_index.In | 48 |
| abstract_inverted_index.On | 102 |
| abstract_inverted_index.an | 99 |
| abstract_inverted_index.as | 86 |
| abstract_inverted_index.at | 134 |
| abstract_inverted_index.by | 61 |
| abstract_inverted_index.is | 125, 132 |
| abstract_inverted_index.of | 105 |
| abstract_inverted_index.on | 45, 127 |
| abstract_inverted_index.or | 40, 114, 118 |
| abstract_inverted_index.to | 9, 22, 31, 80 |
| abstract_inverted_index.we | 51 |
| abstract_inverted_index.Our | 70, 130 |
| abstract_inverted_index.and | 35, 78, 94 |
| abstract_inverted_index.any | 16 |
| abstract_inverted_index.new | 11, 24, 63 |
| abstract_inverted_index.our | 109 |
| abstract_inverted_index.out | 104 |
| abstract_inverted_index.the | 120 |
| abstract_inverted_index.been | 29 |
| abstract_inverted_index.code | 131 |
| abstract_inverted_index.have | 28 |
| abstract_inverted_index.like | 3 |
| abstract_inverted_index.over | 89 |
| abstract_inverted_index.test | 96 |
| abstract_inverted_index.that | 56 |
| abstract_inverted_index.this | 33, 49 |
| abstract_inverted_index.with | 91, 98 |
| abstract_inverted_index.Large | 0, 58 |
| abstract_inverted_index.known | 92 |
| abstract_inverted_index.learn | 10 |
| abstract_inverted_index.these | 128 |
| abstract_inverted_index.which | 124 |
| abstract_inverted_index.work, | 50 |
| abstract_inverted_index.Models | 2, 60 |
| abstract_inverted_index.and/or | 43 |
| abstract_inverted_index.detect | 23 |
| abstract_inverted_index.during | 13, 26, 66 |
| abstract_inverted_index.either | 37 |
| abstract_inverted_index.frozen | 74 |
| abstract_inverted_index.label. | 101 |
| abstract_inverted_index.labels | 93 |
| abstract_inverted_index.models | 20 |
| abstract_inverted_index.poorly | 39 |
| abstract_inverted_index.unable | 30 |
| abstract_inverted_index.visual | 19, 64, 84 |
| abstract_inverted_index.ChatGPT | 4 |
| abstract_inverted_index.exceeds | 117 |
| abstract_inverted_index.feature | 76 |
| abstract_inverted_index.instead | 36 |
| abstract_inverted_index.matches | 119 |
| abstract_inverted_index.objects | 25 |
| abstract_inverted_index.perform | 38 |
| abstract_inverted_index.propose | 52 |
| abstract_inverted_index.recasts | 83 |
| abstract_inverted_index.require | 41 |
| abstract_inverted_index.similar | 46 |
| abstract_inverted_index.trained | 21 |
| abstract_inverted_index.unknown | 100 |
| abstract_inverted_index.without | 15, 68, 112 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.Language | 1, 59 |
| abstract_inverted_index.ability, | 34 |
| abstract_inverted_index.approach | 71, 110 |
| abstract_inverted_index.capacity | 8 |
| abstract_inverted_index.concepts | 12, 65 |
| abstract_inverted_index.emulates | 57 |
| abstract_inverted_index.learning | 62 |
| abstract_inverted_index.modeling | 88 |
| abstract_inverted_index.objects. | 47 |
| abstract_inverted_index.sequence | 87 |
| abstract_inverted_index.algorithm | 55 |
| abstract_inverted_index.analogous | 79 |
| abstract_inverted_index.available | 133 |
| abstract_inverted_index.datapoint | 97 |
| abstract_inverted_index.inference | 14, 27, 67 |
| abstract_inverted_index.learning, | 82 |
| abstract_inverted_index.leverages | 72 |
| abstract_inverted_index.replicate | 32 |
| abstract_inverted_index.algorithm, | 122 |
| abstract_inverted_index.datapoints | 90 |
| abstract_inverted_index.extractor, | 77 |
| abstract_inverted_index.in-context | 81 |
| abstract_inverted_index.remarkable | 7 |
| abstract_inverted_index.benchmarks, | 108 |
| abstract_inverted_index.benchmarks. | 129 |
| abstract_inverted_index.demonstrate | 5 |
| abstract_inverted_index.fine-tuning | 44, 115 |
| abstract_inverted_index.pre-trained | 75 |
| abstract_inverted_index.P>M>F, | 123 |
| abstract_inverted_index.fine-tuning. | 17, 69 |
| abstract_inverted_index.meta-trained | 126 |
| abstract_inverted_index.meta-learning | 54, 85, 107 |
| abstract_inverted_index.meta-training | 42, 113 |
| abstract_inverted_index.state-of-the-art | 121 |
| abstract_inverted_index.https://github.com/cfifty/CAML. | 135 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |