Dynamics-Aware Embeddings Article Swipe
In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and actions. These embeddings capture the structure of the environment's dynamics, enabling efficient policy learning. We demonstrate that our action embeddings alone improve the sample efficiency and peak performance of model-free RL on control from low-dimensional states. By combining state and action embeddings, we achieve efficient learning of high-quality policies on goal-conditioned continuous control from pixel observations in only 1-2 million environment steps.
Related Topics
Concepts
Reinforcement learning
Computer science
Artificial intelligence
Representation (politics)
Control (management)
Sample (material)
Dynamics (music)
Action (physics)
Machine learning
Quality (philosophy)
Chemistry
Physics
Law
Political science
Politics
Chromatography
Quantum mechanics
Philosophy
Acoustics
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1908.09357.pdf
- OA Status
- green
- Cited By
- 10
- References
- 44
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2996698477
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2996698477Canonical identifier for this work in OpenAlex
- Title
-
Dynamics-Aware EmbeddingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-30Full publication date if available
- Authors
-
WILLIAM F. WHITNEY, Rajat Agarwal, Kyunghyun Cho, Abhinav GuptaList of authors in order
- Landing page
-
https://arxiv.org/pdf/1908.09357.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1908.09357.pdfDirect OA link when available
- Concepts
-
Reinforcement learning, Computer science, Artificial intelligence, Representation (politics), Control (management), Sample (material), Dynamics (music), Action (physics), Machine learning, Quality (philosophy), Chemistry, Physics, Law, Political science, Politics, Chromatography, Quantum mechanics, Philosophy, Acoustics, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 5, 2020: 4, 2019: 1Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2996698477 |
|---|---|
| doi | |
| ids.mag | 2996698477 |
| ids.openalex | https://openalex.org/W2996698477 |
| fwci | 1.32173594 |
| type | article |
| title | Dynamics-Aware Embeddings |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10462 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994999766349792 |
| 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 | Reinforcement Learning in Robotics |
| topics[1].id | https://openalex.org/T11206 |
| topics[1].field.id | https://openalex.org/fields/31 |
| topics[1].field.display_name | Physics and Astronomy |
| topics[1].score | 0.9872999787330627 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3109 |
| topics[1].subfield.display_name | Statistical and Nonlinear Physics |
| topics[1].display_name | Model Reduction and Neural Networks |
| topics[2].id | https://openalex.org/T11689 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9797999858856201 |
| 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 | Adversarial Robustness in Machine Learning |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C97541855 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8594715595245361 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q830687 |
| concepts[0].display_name | Reinforcement learning |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7764860391616821 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6160587072372437 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C2776359362 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5903013944625854 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2145286 |
| concepts[3].display_name | Representation (politics) |
| concepts[4].id | https://openalex.org/C2775924081 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5315128564834595 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q55608371 |
| concepts[4].display_name | Control (management) |
| concepts[5].id | https://openalex.org/C198531522 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5258198976516724 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q485146 |
| concepts[5].display_name | Sample (material) |
| concepts[6].id | https://openalex.org/C145912823 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5083581805229187 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q113558 |
| concepts[6].display_name | Dynamics (music) |
| concepts[7].id | https://openalex.org/C2780791683 |
| concepts[7].level | 2 |
| concepts[7].score | 0.49163109064102173 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q846785 |
| concepts[7].display_name | Action (physics) |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4879893660545349 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C2779530757 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4777102470397949 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1207505 |
| concepts[9].display_name | Quality (philosophy) |
| concepts[10].id | https://openalex.org/C185592680 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[10].display_name | Chemistry |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C199539241 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[12].display_name | Law |
| concepts[13].id | https://openalex.org/C17744445 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[13].display_name | Political science |
| 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/C43617362 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q170050 |
| concepts[15].display_name | Chromatography |
| concepts[16].id | https://openalex.org/C62520636 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[16].display_name | Quantum mechanics |
| concepts[17].id | https://openalex.org/C138885662 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[17].display_name | Philosophy |
| concepts[18].id | https://openalex.org/C24890656 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q82811 |
| concepts[18].display_name | Acoustics |
| concepts[19].id | https://openalex.org/C111472728 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[19].display_name | Epistemology |
| keywords[0].id | https://openalex.org/keywords/reinforcement-learning |
| keywords[0].score | 0.8594715595245361 |
| keywords[0].display_name | Reinforcement learning |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7764860391616821 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6160587072372437 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/representation |
| keywords[3].score | 0.5903013944625854 |
| keywords[3].display_name | Representation (politics) |
| keywords[4].id | https://openalex.org/keywords/control |
| keywords[4].score | 0.5315128564834595 |
| keywords[4].display_name | Control (management) |
| keywords[5].id | https://openalex.org/keywords/sample |
| keywords[5].score | 0.5258198976516724 |
| keywords[5].display_name | Sample (material) |
| keywords[6].id | https://openalex.org/keywords/dynamics |
| keywords[6].score | 0.5083581805229187 |
| keywords[6].display_name | Dynamics (music) |
| keywords[7].id | https://openalex.org/keywords/action |
| keywords[7].score | 0.49163109064102173 |
| keywords[7].display_name | Action (physics) |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.4879893660545349 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/quality |
| keywords[9].score | 0.4777102470397949 |
| keywords[9].display_name | Quality (philosophy) |
| language | en |
| locations[0].id | mag:2996698477 |
| 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 | |
| locations[0].version | |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | arXiv (Cornell University) |
| locations[0].landing_page_url | https://arxiv.org/pdf/1908.09357.pdf |
| authorships[0].author.id | https://openalex.org/A5005235934 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | WILLIAM F. WHITNEY |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I57206974 |
| authorships[0].affiliations[0].raw_affiliation_string | New York University, New York, United States |
| authorships[0].institutions[0].id | https://openalex.org/I57206974 |
| authorships[0].institutions[0].ror | https://ror.org/0190ak572 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I57206974 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | New York University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | William Whitney |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | New York University, New York, United States |
| authorships[1].author.id | https://openalex.org/A5084653029 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4864-6479 |
| authorships[1].author.display_name | Rajat Agarwal |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Rajat Agarwal |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5091175785 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1669-3211 |
| authorships[2].author.display_name | Kyunghyun Cho |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I57206974 |
| authorships[2].affiliations[0].raw_affiliation_string | New York University, New York, United States |
| authorships[2].institutions[0].id | https://openalex.org/I57206974 |
| authorships[2].institutions[0].ror | https://ror.org/0190ak572 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I57206974 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | New York University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Kyunghyun Cho |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | New York University, New York, United States |
| authorships[3].author.id | https://openalex.org/A5101761266 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3646-2421 |
| authorships[3].author.display_name | Abhinav Gupta |
| authorships[3].countries | IL |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I2252078561 |
| authorships[3].affiliations[0].raw_affiliation_string | Meta (Israel), Tel Aviv, Israel |
| authorships[3].institutions[0].id | https://openalex.org/I2252078561 |
| authorships[3].institutions[0].ror | https://ror.org/02388em19 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I2252078561, https://openalex.org/I4210114444 |
| authorships[3].institutions[0].country_code | IL |
| authorships[3].institutions[0].display_name | Meta (Israel) |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Abhinav Gupta |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Meta (Israel), Tel Aviv, Israel |
| 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/1908.09357.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Dynamics-Aware Embeddings |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-10-10T17:16:08.811792 |
| primary_topic.id | https://openalex.org/T10462 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994999766349792 |
| 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 | Reinforcement Learning in Robotics |
| related_works | https://openalex.org/W2969520741, https://openalex.org/W2121863487, https://openalex.org/W2964158321, https://openalex.org/W1959608418, https://openalex.org/W2014081627, https://openalex.org/W2936107880, https://openalex.org/W2166265228, https://openalex.org/W1526807135, https://openalex.org/W3125760305, https://openalex.org/W3136265295, https://openalex.org/W2038771780, https://openalex.org/W3127235394, https://openalex.org/W2105486945, https://openalex.org/W3034752558, https://openalex.org/W3211941939, https://openalex.org/W2201042428, https://openalex.org/W2964161785, https://openalex.org/W2964121744, https://openalex.org/W3167527548, https://openalex.org/W1982948368 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2021 |
| counts_by_year[0].cited_by_count | 5 |
| counts_by_year[1].year | 2020 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2019 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | mag:2996698477 |
| 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 | |
| best_oa_location.version | |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | arXiv (Cornell University) |
| best_oa_location.landing_page_url | https://arxiv.org/pdf/1908.09357.pdf |
| primary_location.id | mag:2996698477 |
| 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 | |
| primary_location.version | |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | arXiv (Cornell University) |
| primary_location.landing_page_url | https://arxiv.org/pdf/1908.09357.pdf |
| publication_date | 2020-04-30 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2173248099, https://openalex.org/W2895777761, https://openalex.org/W2786036274, https://openalex.org/W2963262099, https://openalex.org/W2964332824, https://openalex.org/W2109910161, https://openalex.org/W2964158321, https://openalex.org/W2897239789, https://openalex.org/W2173520492, https://openalex.org/W2753738274, https://openalex.org/W1686946872, https://openalex.org/W2557579533, https://openalex.org/W2736601468, https://openalex.org/W1909320841, https://openalex.org/W2785342287, https://openalex.org/W2894605519, https://openalex.org/W2796206912, https://openalex.org/W2990747716, https://openalex.org/W2567455162, https://openalex.org/W2396672775, https://openalex.org/W2417089653, https://openalex.org/W2781726626, https://openalex.org/W2787938642, https://openalex.org/W2806098286, https://openalex.org/W2964121744, https://openalex.org/W2215378786, https://openalex.org/W2165150801, https://openalex.org/W2951670304, https://openalex.org/W2962717849, https://openalex.org/W2606433045, https://openalex.org/W2904246096, https://openalex.org/W2964227312, https://openalex.org/W2158782408, https://openalex.org/W2594829461, https://openalex.org/W2810785043, https://openalex.org/W2950872548, https://openalex.org/W2121863487, https://openalex.org/W2963674921, https://openalex.org/W2798705390, https://openalex.org/W2953319434, https://openalex.org/W2900152462, https://openalex.org/W1959608418, https://openalex.org/W2056354534, https://openalex.org/W2951066214 |
| referenced_works_count | 44 |
| abstract_inverted_index.a | 18 |
| abstract_inverted_index.By | 65 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.RL | 59 |
| abstract_inverted_index.We | 16, 43 |
| abstract_inverted_index.in | 12, 85 |
| abstract_inverted_index.of | 26, 35, 57, 75 |
| abstract_inverted_index.on | 60, 78 |
| abstract_inverted_index.to | 8 |
| abstract_inverted_index.we | 3, 71 |
| abstract_inverted_index.1-2 | 87 |
| abstract_inverted_index.and | 28, 54, 68 |
| abstract_inverted_index.for | 22 |
| abstract_inverted_index.our | 46 |
| abstract_inverted_index.the | 33, 36, 51 |
| abstract_inverted_index.from | 62, 82 |
| abstract_inverted_index.only | 86 |
| abstract_inverted_index.peak | 55 |
| abstract_inverted_index.that | 45 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.(RL). | 15 |
| abstract_inverted_index.These | 30 |
| abstract_inverted_index.alone | 49 |
| abstract_inverted_index.paper | 2 |
| abstract_inverted_index.pixel | 83 |
| abstract_inverted_index.state | 67 |
| abstract_inverted_index.action | 47, 69 |
| abstract_inverted_index.policy | 41 |
| abstract_inverted_index.sample | 10, 52 |
| abstract_inverted_index.states | 27 |
| abstract_inverted_index.steps. | 90 |
| abstract_inverted_index.achieve | 72 |
| abstract_inverted_index.capture | 32 |
| abstract_inverted_index.control | 61, 81 |
| abstract_inverted_index.forward | 19 |
| abstract_inverted_index.improve | 9, 50 |
| abstract_inverted_index.million | 88 |
| abstract_inverted_index.propose | 17 |
| abstract_inverted_index.states. | 64 |
| abstract_inverted_index.actions. | 29 |
| abstract_inverted_index.consider | 4 |
| abstract_inverted_index.enabling | 39 |
| abstract_inverted_index.learning | 7, 14, 24, 74 |
| abstract_inverted_index.policies | 77 |
| abstract_inverted_index.combining | 66 |
| abstract_inverted_index.dynamics, | 38 |
| abstract_inverted_index.efficient | 40, 73 |
| abstract_inverted_index.learning. | 42 |
| abstract_inverted_index.objective | 21 |
| abstract_inverted_index.structure | 34 |
| abstract_inverted_index.continuous | 80 |
| abstract_inverted_index.efficiency | 11, 53 |
| abstract_inverted_index.embeddings | 25, 31, 48 |
| abstract_inverted_index.model-free | 58 |
| abstract_inverted_index.prediction | 20 |
| abstract_inverted_index.demonstrate | 44 |
| abstract_inverted_index.embeddings, | 70 |
| abstract_inverted_index.environment | 89 |
| abstract_inverted_index.performance | 56 |
| abstract_inverted_index.high-quality | 76 |
| abstract_inverted_index.observations | 84 |
| abstract_inverted_index.environment's | 37 |
| abstract_inverted_index.reinforcement | 13 |
| abstract_inverted_index.representation | 6 |
| abstract_inverted_index.simultaneously | 23 |
| abstract_inverted_index.low-dimensional | 63 |
| abstract_inverted_index.self-supervised | 5 |
| abstract_inverted_index.goal-conditioned | 79 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.83569664 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |