Adaptive Neural Trees Article Swipe
Ryutaro Tanno
,
Kai Arulkumaran
,
Daniel C. Alexander
,
Antonio Criminisi
,
Aditya V. Nori
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1807.06699
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1807.06699
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neural trees (ANTs) that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers). We demonstrate that, whilst achieving competitive performance on classification and regression datasets, ANTs benefit from (i) lightweight inference via conditional computation, (ii) hierarchical separation of features useful to the task e.g. learning meaningful class associations, such as separating natural vs. man-made objects, and (iii) a mechanism to adapt the architecture to the size and complexity of the training dataset.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://arxiv.org/abs/1807.06699
- https://arxiv.org/pdf/1807.06699
- OA Status
- green
- Cited By
- 24
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2886214919
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2886214919Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1807.06699Digital Object Identifier
- Title
-
Adaptive Neural TreesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-07-17Full publication date if available
- Authors
-
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. NoriList of authors in order
- Landing page
-
https://arxiv.org/abs/1807.06699Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1807.06699Direct 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/1807.06699Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Machine learning, Decision tree, Inference, Representation (politics), Backpropagation, Artificial neural network, Tree (set theory), Deep learning, Convolutional neural network, Class (philosophy), Mathematics, Mathematical analysis, Politics, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 16, 2020: 6, 2019: 1Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2886214919 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.1807.06699 |
| ids.doi | https://doi.org/10.48550/arxiv.1807.06699 |
| ids.mag | 2886214919 |
| ids.openalex | https://openalex.org/W2886214919 |
| fwci | |
| type | article |
| title | Adaptive Neural Trees |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 6175 |
| biblio.first_page | 6166 |
| topics[0].id | https://openalex.org/T12026 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9979000091552734 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Explainable Artificial Intelligence (XAI) |
| topics[1].id | https://openalex.org/T10320 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9940000176429749 |
| 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 | Neural Networks and Applications |
| topics[2].id | https://openalex.org/T12535 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9937999844551086 |
| 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 | Machine Learning and Data Classification |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.797490119934082 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7039337754249573 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C119857082 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6251466870307922 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[2].display_name | Machine learning |
| concepts[3].id | https://openalex.org/C84525736 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5628500580787659 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q831366 |
| concepts[3].display_name | Decision tree |
| concepts[4].id | https://openalex.org/C2776214188 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5485906004905701 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[4].display_name | Inference |
| concepts[5].id | https://openalex.org/C2776359362 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5294299721717834 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2145286 |
| concepts[5].display_name | Representation (politics) |
| concepts[6].id | https://openalex.org/C155032097 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5175401568412781 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q798503 |
| concepts[6].display_name | Backpropagation |
| concepts[7].id | https://openalex.org/C50644808 |
| concepts[7].level | 2 |
| concepts[7].score | 0.487708181142807 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[7].display_name | Artificial neural network |
| concepts[8].id | https://openalex.org/C113174947 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4687885046005249 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2859736 |
| concepts[8].display_name | Tree (set theory) |
| concepts[9].id | https://openalex.org/C108583219 |
| concepts[9].level | 2 |
| concepts[9].score | 0.46297141909599304 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[9].display_name | Deep learning |
| concepts[10].id | https://openalex.org/C81363708 |
| concepts[10].level | 2 |
| concepts[10].score | 0.45677417516708374 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[10].display_name | Convolutional neural network |
| concepts[11].id | https://openalex.org/C2777212361 |
| concepts[11].level | 2 |
| concepts[11].score | 0.45145758986473083 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q5127848 |
| concepts[11].display_name | Class (philosophy) |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.08419892191886902 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C134306372 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[13].display_name | Mathematical analysis |
| concepts[14].id | https://openalex.org/C94625758 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7163 |
| concepts[14].display_name | Politics |
| concepts[15].id | https://openalex.org/C17744445 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[15].display_name | Political science |
| concepts[16].id | https://openalex.org/C199539241 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[16].display_name | Law |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.797490119934082 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.7039337754249573 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/machine-learning |
| keywords[2].score | 0.6251466870307922 |
| keywords[2].display_name | Machine learning |
| keywords[3].id | https://openalex.org/keywords/decision-tree |
| keywords[3].score | 0.5628500580787659 |
| keywords[3].display_name | Decision tree |
| keywords[4].id | https://openalex.org/keywords/inference |
| keywords[4].score | 0.5485906004905701 |
| keywords[4].display_name | Inference |
| keywords[5].id | https://openalex.org/keywords/representation |
| keywords[5].score | 0.5294299721717834 |
| keywords[5].display_name | Representation (politics) |
| keywords[6].id | https://openalex.org/keywords/backpropagation |
| keywords[6].score | 0.5175401568412781 |
| keywords[6].display_name | Backpropagation |
| keywords[7].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[7].score | 0.487708181142807 |
| keywords[7].display_name | Artificial neural network |
| keywords[8].id | https://openalex.org/keywords/tree |
| keywords[8].score | 0.4687885046005249 |
| keywords[8].display_name | Tree (set theory) |
| keywords[9].id | https://openalex.org/keywords/deep-learning |
| keywords[9].score | 0.46297141909599304 |
| keywords[9].display_name | Deep learning |
| keywords[10].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[10].score | 0.45677417516708374 |
| keywords[10].display_name | Convolutional neural network |
| keywords[11].id | https://openalex.org/keywords/class |
| keywords[11].score | 0.45145758986473083 |
| keywords[11].display_name | Class (philosophy) |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.08419892191886902 |
| keywords[12].display_name | Mathematics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:1807.06699 |
| 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/1807.06699 |
| 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/1807.06699 |
| locations[1].id | pmh:oai:spiral.imperial.ac.uk:10044/1/71619 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401396 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Spiral (Imperial College London) |
| locations[1].source.host_organization | https://openalex.org/I47508984 |
| locations[1].source.host_organization_name | Imperial College London |
| locations[1].source.host_organization_lineage | https://openalex.org/I47508984 |
| locations[1].license | |
| locations[1].pdf_url | http://hdl.handle.net/10044/1/71619 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Working Paper |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://hdl.handle.net/10044/1/71619 |
| locations[2].id | mag:2950149565 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | arXiv (Cornell University) |
| locations[2].landing_page_url | http://export.arxiv.org/pdf/1807.06699 |
| locations[3].id | pmh:oai:eprints.ucl.ac.uk.OAI2:10092807 |
| locations[3].is_oa | False |
| locations[3].source.id | https://openalex.org/S4306400024 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | UCL Discovery (University College London) |
| locations[3].source.host_organization | https://openalex.org/I45129253 |
| locations[3].source.host_organization_name | University College London |
| locations[3].source.host_organization_lineage | https://openalex.org/I45129253 |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Proceedings paper |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | In: Proceedings of the 36th International Conference on Machine Learning. (pp. pp. 6166-6175). Proceedings of Machine Learning Research (2019) |
| locations[3].landing_page_url | https://discovery.ucl.ac.uk/id/eprint/10092807/ |
| locations[4].id | doi:10.48550/arxiv.1807.06699 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306400194 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | True |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | arXiv (Cornell University) |
| locations[4].source.host_organization | https://openalex.org/I205783295 |
| locations[4].source.host_organization_name | Cornell University |
| locations[4].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[4].license | |
| locations[4].pdf_url | |
| locations[4].version | |
| locations[4].raw_type | article |
| locations[4].license_id | |
| locations[4].is_accepted | False |
| locations[4].is_published | |
| locations[4].raw_source_name | |
| locations[4].landing_page_url | https://doi.org/10.48550/arxiv.1807.06699 |
| locations[5].id | mag:2886214919 |
| locations[5].is_oa | False |
| locations[5].source.id | https://openalex.org/S4306419644 |
| locations[5].source.issn | |
| locations[5].source.type | conference |
| locations[5].source.is_oa | False |
| locations[5].source.issn_l | |
| locations[5].source.is_core | False |
| locations[5].source.is_in_doaj | False |
| locations[5].source.display_name | International Conference on Machine Learning |
| locations[5].source.host_organization | |
| locations[5].source.host_organization_name | |
| locations[5].license | |
| locations[5].pdf_url | |
| locations[5].version | |
| locations[5].raw_type | |
| locations[5].license_id | |
| locations[5].is_accepted | False |
| locations[5].is_published | |
| locations[5].raw_source_name | International Conference on Machine Learning |
| locations[5].landing_page_url | http://proceedings.mlr.press/v97/tanno19a/tanno19a.pdf |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5064157732 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8107-6730 |
| authorships[0].author.display_name | Ryutaro Tanno |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I45129253 |
| authorships[0].affiliations[0].raw_affiliation_string | University College London, London, United Kingdom |
| authorships[0].institutions[0].id | https://openalex.org/I45129253 |
| authorships[0].institutions[0].ror | https://ror.org/02jx3x895 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I45129253 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | University College London |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ryutaro Tanno |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University College London, London, United Kingdom |
| authorships[1].author.id | https://openalex.org/A5065210301 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0459-892X |
| authorships[1].author.display_name | Kai Arulkumaran |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I47508984 |
| authorships[1].affiliations[0].raw_affiliation_string | Imperial College London, London, United Kingdom |
| authorships[1].institutions[0].id | https://openalex.org/I47508984 |
| authorships[1].institutions[0].ror | https://ror.org/041kmwe10 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I47508984 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | Imperial College London |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Kai Arulkumaran |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Imperial College London, London, United Kingdom |
| authorships[2].author.id | https://openalex.org/A5033449704 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2439-350X |
| authorships[2].author.display_name | Daniel C. Alexander |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I45129253 |
| authorships[2].affiliations[0].raw_affiliation_string | University College London, London, United Kingdom |
| authorships[2].institutions[0].id | https://openalex.org/I45129253 |
| authorships[2].institutions[0].ror | https://ror.org/02jx3x895 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I45129253 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | University College London |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Daniel C. Alexander |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | University College London, London, United Kingdom |
| authorships[3].author.id | https://openalex.org/A5091150085 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7976-3374 |
| authorships[3].author.display_name | Antonio Criminisi |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I1290206253 |
| authorships[3].affiliations[0].raw_affiliation_string | Microsoft (United States), Redmond, United States |
| authorships[3].institutions[0].id | https://openalex.org/I1290206253 |
| authorships[3].institutions[0].ror | https://ror.org/00d0nc645 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I1290206253 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Microsoft (United States) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Antonio Criminisi |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Microsoft (United States), Redmond, United States |
| authorships[4].author.id | https://openalex.org/A5111937381 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Aditya V. Nori |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1290206253 |
| authorships[4].affiliations[0].raw_affiliation_string | Microsoft (United States), Redmond, United States |
| authorships[4].institutions[0].id | https://openalex.org/I1290206253 |
| authorships[4].institutions[0].ror | https://ror.org/00d0nc645 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I1290206253 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Microsoft (United States) |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Aditya V. Nori |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Microsoft (United States), Redmond, United States |
| 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/1807.06699 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Adaptive Neural Trees |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12026 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9979000091552734 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Explainable Artificial Intelligence (XAI) |
| related_works | https://openalex.org/W2769421449, https://openalex.org/W2220384803, https://openalex.org/W2194775991, https://openalex.org/W3034933916, https://openalex.org/W1836465849, https://openalex.org/W1594031697, https://openalex.org/W2541218906, https://openalex.org/W1510445422, https://openalex.org/W2968898024, https://openalex.org/W2963055657, https://openalex.org/W2167179522, https://openalex.org/W2156181664, https://openalex.org/W2542042057, https://openalex.org/W3099829419, https://openalex.org/W2795798526, https://openalex.org/W2919681562, https://openalex.org/W2101234009, https://openalex.org/W2025653905, https://openalex.org/W1821462560, https://openalex.org/W1797268635 |
| cited_by_count | 24 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2021 |
| counts_by_year[1].cited_by_count | 16 |
| counts_by_year[2].year | 2020 |
| counts_by_year[2].cited_by_count | 6 |
| counts_by_year[3].year | 2019 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 6 |
| best_oa_location.id | pmh:oai:arXiv.org:1807.06699 |
| 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/1807.06699 |
| 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/1807.06699 |
| primary_location.id | pmh:oai:arXiv.org:1807.06699 |
| 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/1807.06699 |
| 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/1807.06699 |
| publication_date | 2018-07-17 |
| publication_year | 2018 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 55, 60, 119 |
| abstract_inverted_index.We | 34, 75 |
| abstract_inverted_index.as | 111 |
| abstract_inverted_index.by | 25 |
| abstract_inverted_index.is | 23 |
| abstract_inverted_index.of | 54, 99, 130 |
| abstract_inverted_index.on | 7, 82 |
| abstract_inverted_index.to | 102, 121, 125 |
| abstract_inverted_index.(i) | 90 |
| abstract_inverted_index.and | 3, 51, 84, 117, 128 |
| abstract_inverted_index.the | 12, 21, 36, 67, 103, 123, 126, 131 |
| abstract_inverted_index.two | 37 |
| abstract_inverted_index.via | 38, 93 |
| abstract_inverted_index.vs. | 114 |
| abstract_inverted_index.(ii) | 96 |
| abstract_inverted_index.ANTs | 87 |
| abstract_inverted_index.Deep | 0 |
| abstract_inverted_index.e.g. | 105 |
| abstract_inverted_index.from | 69, 89 |
| abstract_inverted_index.into | 47 |
| abstract_inverted_index.leaf | 52 |
| abstract_inverted_index.over | 28 |
| abstract_inverted_index.size | 127 |
| abstract_inverted_index.such | 110 |
| abstract_inverted_index.task | 104 |
| abstract_inverted_index.that | 43, 64 |
| abstract_inverted_index.with | 17, 31, 59 |
| abstract_inverted_index.(iii) | 118 |
| abstract_inverted_index.adapt | 122 |
| abstract_inverted_index.along | 58 |
| abstract_inverted_index.class | 108 |
| abstract_inverted_index.grows | 66 |
| abstract_inverted_index.nodes | 53 |
| abstract_inverted_index.that, | 77 |
| abstract_inverted_index.tree, | 57 |
| abstract_inverted_index.trees | 5, 41 |
| abstract_inverted_index.unite | 35 |
| abstract_inverted_index.while | 20 |
| abstract_inverted_index.(ANTs) | 42 |
| abstract_inverted_index.(e.g., | 72 |
| abstract_inverted_index.edges, | 48 |
| abstract_inverted_index.former | 13 |
| abstract_inverted_index.latter | 22 |
| abstract_inverted_index.neural | 1, 40 |
| abstract_inverted_index.useful | 101 |
| abstract_inverted_index.whilst | 78 |
| abstract_inverted_index.benefit | 88 |
| abstract_inverted_index.largely | 8 |
| abstract_inverted_index.modules | 71 |
| abstract_inverted_index.natural | 113 |
| abstract_inverted_index.operate | 6 |
| abstract_inverted_index.routing | 49 |
| abstract_inverted_index.adaptive | 39 |
| abstract_inverted_index.dataset. | 133 |
| abstract_inverted_index.decision | 4, 56 |
| abstract_inverted_index.features | 30, 100 |
| abstract_inverted_index.layers). | 74 |
| abstract_inverted_index.learning | 16, 26, 46, 106 |
| abstract_inverted_index.man-made | 115 |
| abstract_inverted_index.networks | 2 |
| abstract_inverted_index.objects, | 116 |
| abstract_inverted_index.performs | 14 |
| abstract_inverted_index.separate | 9 |
| abstract_inverted_index.training | 62, 132 |
| abstract_inverted_index.achieving | 79 |
| abstract_inverted_index.algorithm | 63 |
| abstract_inverted_index.datasets, | 86 |
| abstract_inverted_index.functions | 50 |
| abstract_inverted_index.inference | 92 |
| abstract_inverted_index.mechanism | 120 |
| abstract_inverted_index.primitive | 70 |
| abstract_inverted_index.adaptively | 65 |
| abstract_inverted_index.complexity | 129 |
| abstract_inverted_index.meaningful | 107 |
| abstract_inverted_index.paradigms; | 10 |
| abstract_inverted_index.regression | 85 |
| abstract_inverted_index.separating | 112 |
| abstract_inverted_index.separation | 98 |
| abstract_inverted_index.typically, | 11 |
| abstract_inverted_index.competitive | 80 |
| abstract_inverted_index.conditional | 94 |
| abstract_inverted_index.data-driven | 32 |
| abstract_inverted_index.demonstrate | 76 |
| abstract_inverted_index.hierarchies | 27 |
| abstract_inverted_index.lightweight | 91 |
| abstract_inverted_index.performance | 81 |
| abstract_inverted_index.architecture | 68, 124 |
| abstract_inverted_index.computation, | 95 |
| abstract_inverted_index.hierarchical | 97 |
| abstract_inverted_index.incorporates | 44 |
| abstract_inverted_index.associations, | 109 |
| abstract_inverted_index.characterised | 24 |
| abstract_inverted_index.convolutional | 73 |
| abstract_inverted_index.pre-specified | 18, 29 |
| abstract_inverted_index.architectures, | 19 |
| abstract_inverted_index.architectures. | 33 |
| abstract_inverted_index.classification | 83 |
| abstract_inverted_index.representation | 15, 45 |
| abstract_inverted_index.backpropagation-based | 61 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |