Determinantal point process attention over grid cell code supports out of distribution generalization Article Swipe
Shanka Subhra Mondal
,
Steven Frankland
,
Taylor W. Webb
,
Jonathan D. Cohen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7554/elife.89911.3
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7554/elife.89911.3
Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization – successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) – a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.89911.3
- OA Status
- gold
- Cited By
- 2
- References
- 87
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401215766
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401215766Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7554/elife.89911.3Digital Object Identifier
- Title
-
Determinantal point process attention over grid cell code supports out of distribution generalizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-01Full publication date if available
- Authors
-
Shanka Subhra Mondal, Steven Frankland, Taylor W. Webb, Jonathan D. CohenList of authors in order
- Landing page
-
https://doi.org/10.7554/elife.89911.3Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7554/elife.89911.3Direct OA link when available
- Concepts
-
Generalization, Computer science, Grid, Artificial intelligence, Theoretical computer science, Code (set theory), Set (abstract data type), Mathematics, Mathematical analysis, Programming language, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
87Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401215766 |
|---|---|
| doi | https://doi.org/10.7554/elife.89911.3 |
| ids.doi | https://doi.org/10.7554/elife.89911.3 |
| ids.openalex | https://openalex.org/W4401215766 |
| fwci | 1.40560113 |
| type | article |
| title | Determinantal point process attention over grid cell code supports out of distribution generalization |
| biblio.issue | |
| biblio.volume | 12 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10448 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.998199999332428 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | Memory and Neural Mechanisms |
| topics[1].id | https://openalex.org/T10581 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.9968000054359436 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2805 |
| topics[1].subfield.display_name | Cognitive Neuroscience |
| topics[1].display_name | Neural dynamics and brain function |
| topics[2].id | https://openalex.org/T10627 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9850999712944031 |
| 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 | Advanced Image and Video Retrieval Techniques |
| funders[0].id | https://openalex.org/F4320337345 |
| funders[0].ror | https://ror.org/00rk2pe57 |
| funders[0].display_name | Office of Naval Research |
| is_xpac | False |
| apc_list.value | 3000 |
| apc_list.currency | USD |
| apc_list.value_usd | 2500 |
| apc_paid.value | 3000 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2500 |
| concepts[0].id | https://openalex.org/C177148314 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7109836935997009 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q170084 |
| concepts[0].display_name | Generalization |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.691190242767334 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C187691185 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5282163619995117 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2020720 |
| concepts[2].display_name | Grid |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5164740085601807 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C80444323 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4446971118450165 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[4].display_name | Theoretical computer science |
| concepts[5].id | https://openalex.org/C2776760102 |
| concepts[5].level | 3 |
| concepts[5].score | 0.4299848973751068 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5139990 |
| concepts[5].display_name | Code (set theory) |
| concepts[6].id | https://openalex.org/C177264268 |
| concepts[6].level | 2 |
| concepts[6].score | 0.42164507508277893 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[6].display_name | Set (abstract data type) |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.1264975666999817 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C134306372 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[8].display_name | Mathematical analysis |
| concepts[9].id | https://openalex.org/C199360897 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[9].display_name | Programming language |
| concepts[10].id | https://openalex.org/C2524010 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[10].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/generalization |
| keywords[0].score | 0.7109836935997009 |
| keywords[0].display_name | Generalization |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.691190242767334 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/grid |
| keywords[2].score | 0.5282163619995117 |
| keywords[2].display_name | Grid |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5164740085601807 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[4].score | 0.4446971118450165 |
| keywords[4].display_name | Theoretical computer science |
| keywords[5].id | https://openalex.org/keywords/code |
| keywords[5].score | 0.4299848973751068 |
| keywords[5].display_name | Code (set theory) |
| keywords[6].id | https://openalex.org/keywords/set |
| keywords[6].score | 0.42164507508277893 |
| keywords[6].display_name | Set (abstract data type) |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.1264975666999817 |
| keywords[7].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.7554/elife.89911.3 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S1336409049 |
| locations[0].source.issn | 2050-084X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2050-084X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | eLife |
| locations[0].source.host_organization | https://openalex.org/P4310311710 |
| locations[0].source.host_organization_name | eLife Sciences Publications Ltd |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311710 |
| locations[0].source.host_organization_lineage_names | eLife Sciences Publications Ltd |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | eLife |
| locations[0].landing_page_url | https://doi.org/10.7554/elife.89911.3 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5029266882 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1252-0129 |
| authorships[0].author.display_name | Shanka Subhra Mondal |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I20089843 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Electrical and Computer Engineering, Princeton University |
| authorships[0].institutions[0].id | https://openalex.org/I20089843 |
| authorships[0].institutions[0].ror | https://ror.org/00hx57361 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I20089843 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Princeton University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shanka Subhra Mondal |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Electrical and Computer Engineering, Princeton University |
| authorships[1].author.id | https://openalex.org/A5045332730 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5326-0680 |
| authorships[1].author.display_name | Steven Frankland |
| authorships[1].countries | IT, US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I20089843, https://openalex.org/I4210113153 |
| authorships[1].affiliations[0].raw_affiliation_string | Princeton Neuroscience Institute, Princeton University |
| authorships[1].institutions[0].id | https://openalex.org/I4210113153 |
| authorships[1].institutions[0].ror | https://ror.org/0240rwx68 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210113153, https://openalex.org/I4210155236 |
| authorships[1].institutions[0].country_code | IT |
| authorships[1].institutions[0].display_name | Neuroscience Institute |
| authorships[1].institutions[1].id | https://openalex.org/I20089843 |
| authorships[1].institutions[1].ror | https://ror.org/00hx57361 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I20089843 |
| authorships[1].institutions[1].country_code | US |
| authorships[1].institutions[1].display_name | Princeton University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Steven Frankland |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Princeton Neuroscience Institute, Princeton University |
| authorships[2].author.id | https://openalex.org/A5079675053 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1335-3177 |
| authorships[2].author.display_name | Taylor W. Webb |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I161318765 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Psychology, University of California, Los Angeles |
| authorships[2].institutions[0].id | https://openalex.org/I161318765 |
| authorships[2].institutions[0].ror | https://ror.org/046rm7j60 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I161318765 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of California, Los Angeles |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Taylor W Webb |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Psychology, University of California, Los Angeles |
| authorships[3].author.id | https://openalex.org/A5088141761 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2316-0763 |
| authorships[3].author.display_name | Jonathan D. Cohen |
| authorships[3].countries | IT, US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I20089843, https://openalex.org/I4210113153 |
| authorships[3].affiliations[0].raw_affiliation_string | Princeton Neuroscience Institute, Princeton University |
| authorships[3].institutions[0].id | https://openalex.org/I4210113153 |
| authorships[3].institutions[0].ror | https://ror.org/0240rwx68 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210113153, https://openalex.org/I4210155236 |
| authorships[3].institutions[0].country_code | IT |
| authorships[3].institutions[0].display_name | Neuroscience Institute |
| authorships[3].institutions[1].id | https://openalex.org/I20089843 |
| authorships[3].institutions[1].ror | https://ror.org/00hx57361 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I20089843 |
| authorships[3].institutions[1].country_code | US |
| authorships[3].institutions[1].display_name | Princeton University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Jonathan D Cohen |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Princeton Neuroscience Institute, Princeton University |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.7554/elife.89911.3 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Determinantal point process attention over grid cell code supports out of distribution generalization |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10448 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.998199999332428 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | Memory and Neural Mechanisms |
| related_works | https://openalex.org/W3162204513, https://openalex.org/W2371138613, https://openalex.org/W2048963458, https://openalex.org/W43109613, https://openalex.org/W2359952343, https://openalex.org/W2239445980, https://openalex.org/W2080152487, https://openalex.org/W3083152911, https://openalex.org/W3022347918, https://openalex.org/W4200527723 |
| cited_by_count | 2 |
| 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 | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.7554/elife.89911.3 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S1336409049 |
| best_oa_location.source.issn | 2050-084X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2050-084X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | eLife |
| best_oa_location.source.host_organization | https://openalex.org/P4310311710 |
| best_oa_location.source.host_organization_name | eLife Sciences Publications Ltd |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311710 |
| best_oa_location.source.host_organization_lineage_names | eLife Sciences Publications Ltd |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | eLife |
| best_oa_location.landing_page_url | https://doi.org/10.7554/elife.89911.3 |
| primary_location.id | doi:10.7554/elife.89911.3 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S1336409049 |
| primary_location.source.issn | 2050-084X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2050-084X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | eLife |
| primary_location.source.host_organization | https://openalex.org/P4310311710 |
| primary_location.source.host_organization_name | eLife Sciences Publications Ltd |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311710 |
| primary_location.source.host_organization_lineage_names | eLife Sciences Publications Ltd |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | eLife |
| primary_location.landing_page_url | https://doi.org/10.7554/elife.89911.3 |
| publication_date | 2024-08-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2599683001, https://openalex.org/W6780226713, https://openalex.org/W2800142021, https://openalex.org/W2936249354, https://openalex.org/W6752056529, https://openalex.org/W1968533788, https://openalex.org/W2080994882, https://openalex.org/W2921887123, https://openalex.org/W6843491963, https://openalex.org/W2037343688, https://openalex.org/W6606409269, https://openalex.org/W1540763494, https://openalex.org/W4389108910, https://openalex.org/W6751707208, https://openalex.org/W2131956332, https://openalex.org/W2434898965, https://openalex.org/W6747967427, https://openalex.org/W2056354534, https://openalex.org/W6755207826, https://openalex.org/W2081197259, https://openalex.org/W2323385084, https://openalex.org/W2041152585, https://openalex.org/W2116704462, https://openalex.org/W6768238715, https://openalex.org/W6791139735, https://openalex.org/W6688167117, https://openalex.org/W6683355481, https://openalex.org/W2019054821, https://openalex.org/W6677302653, https://openalex.org/W1970792572, https://openalex.org/W6736202428, https://openalex.org/W2194775991, https://openalex.org/W2963150697, https://openalex.org/W6758488800, https://openalex.org/W2955532351, https://openalex.org/W6680230698, https://openalex.org/W2625237928, https://openalex.org/W2087064277, https://openalex.org/W1836465849, https://openalex.org/W6631190155, https://openalex.org/W2147922978, https://openalex.org/W2098642746, https://openalex.org/W6681151457, https://openalex.org/W6680514831, https://openalex.org/W6748655984, https://openalex.org/W3150839556, https://openalex.org/W2006672084, https://openalex.org/W6738121732, https://openalex.org/W6757834963, https://openalex.org/W2121881115, https://openalex.org/W2047057213, https://openalex.org/W4311850018, https://openalex.org/W2142449474, https://openalex.org/W6756040250, https://openalex.org/W3182483462, https://openalex.org/W3135321662, https://openalex.org/W1997423404, https://openalex.org/W6758604235, https://openalex.org/W2143594200, https://openalex.org/W2766447205, https://openalex.org/W4307583652, https://openalex.org/W2077554996, https://openalex.org/W2951066214, https://openalex.org/W1997692559, https://openalex.org/W2981514504, https://openalex.org/W6739901393, https://openalex.org/W2154446803, https://openalex.org/W6778966052, https://openalex.org/W4386120668, https://openalex.org/W2178512648, https://openalex.org/W3103493959, https://openalex.org/W6727690538, https://openalex.org/W2952828155, https://openalex.org/W4297940214, https://openalex.org/W2979157300, https://openalex.org/W2951222872, https://openalex.org/W2899771611, https://openalex.org/W4301259831, https://openalex.org/W4386554451, https://openalex.org/W4385245566, https://openalex.org/W2136939460, https://openalex.org/W2115857089, https://openalex.org/W3133973941, https://openalex.org/W157262510, https://openalex.org/W2212703438, https://openalex.org/W2144513243, https://openalex.org/W2158334272 |
| referenced_works_count | 87 |
| abstract_inverted_index.a | 98, 115, 188, 203, 266 |
| abstract_inverted_index.We | 96, 200 |
| abstract_inverted_index.an | 166, 244 |
| abstract_inverted_index.as | 16 |
| abstract_inverted_index.at | 262 |
| abstract_inverted_index.be | 225 |
| abstract_inverted_index.by | 119 |
| abstract_inverted_index.in | 7, 87, 145, 155, 194, 218, 252, 273 |
| abstract_inverted_index.is | 62 |
| abstract_inverted_index.of | 18, 52, 54, 77, 85, 106, 117, 151, 197, 246 |
| abstract_inverted_index.on | 29, 69, 103, 122, 130, 236 |
| abstract_inverted_index.to | 42, 93, 109, 230, 258 |
| abstract_inverted_index.we | 82, 128, 164, 182 |
| abstract_inverted_index.DPP | 184 |
| abstract_inverted_index.OOD | 111, 233 |
| abstract_inverted_index.One | 59 |
| abstract_inverted_index.and | 11, 39, 113, 223, 238, 261 |
| abstract_inverted_index.are | 57 |
| abstract_inverted_index.can | 213, 224 |
| abstract_inverted_index.for | 269 |
| abstract_inverted_index.how | 20, 46, 247 |
| abstract_inverted_index.lie | 73 |
| abstract_inverted_index.may | 23, 91, 256 |
| abstract_inverted_index.of, | 38 |
| abstract_inverted_index.the | 21, 25, 47, 75, 78, 88, 131, 134, 146, 160, 172, 195, 215, 219, 248, 253, 263 |
| abstract_inverted_index.two | 123 |
| abstract_inverted_index.– | 66, 187 |
| abstract_inverted_index.Deep | 0 |
| abstract_inverted_index.This | 241 |
| abstract_inverted_index.been | 13 |
| abstract_inverted_index.both | 243 |
| abstract_inverted_index.call | 183 |
| abstract_inverted_index.case | 61 |
| abstract_inverted_index.cell | 142, 174, 221, 250 |
| abstract_inverted_index.code | 143, 175, 251 |
| abstract_inverted_index.draw | 129 |
| abstract_inverted_index.fact | 132 |
| abstract_inverted_index.fail | 41 |
| abstract_inverted_index.fall | 36 |
| abstract_inverted_index.grid | 141, 173, 220, 249 |
| abstract_inverted_index.have | 3, 12 |
| abstract_inverted_index.into | 45 |
| abstract_inverted_index.loss | 204 |
| abstract_inverted_index.made | 4 |
| abstract_inverted_index.over | 171 |
| abstract_inverted_index.same | 264 |
| abstract_inverted_index.set. | 80 |
| abstract_inverted_index.show | 201 |
| abstract_inverted_index.such | 60, 271 |
| abstract_inverted_index.test | 70 |
| abstract_inverted_index.that | 72, 90, 101, 133, 158, 169, 181, 190, 198, 202, 206 |
| abstract_inverted_index.this | 31, 94 |
| abstract_inverted_index.time | 265 |
| abstract_inverted_index.used | 14 |
| abstract_inverted_index.ways | 17 |
| abstract_inverted_index.with | 211, 227 |
| abstract_inverted_index.(OOD) | 64 |
| abstract_inverted_index.DPP-A | 212 |
| abstract_inverted_index.First | 127 |
| abstract_inverted_index.Here, | 81 |
| abstract_inverted_index.brain | 22, 48, 89, 136, 255 |
| abstract_inverted_index.code, | 222 |
| abstract_inverted_index.cover | 159 |
| abstract_inverted_index.draws | 102 |
| abstract_inverted_index.error | 210 |
| abstract_inverted_index.forms | 51 |
| abstract_inverted_index.gains | 6 |
| abstract_inverted_index.means | 268 |
| abstract_inverted_index.point | 178 |
| abstract_inverted_index.proof | 116 |
| abstract_inverted_index.short | 37 |
| abstract_inverted_index.solve | 24 |
| abstract_inverted_index.still | 35 |
| abstract_inverted_index.these | 34 |
| abstract_inverted_index.using | 140, 176 |
| abstract_inverted_index.which | 30, 55 |
| abstract_inverted_index.(DPP), | 180 |
| abstract_inverted_index.(e.g., | 144 |
| abstract_inverted_index.common | 228 |
| abstract_inverted_index.humans | 56 |
| abstract_inverted_index.metric | 138 |
| abstract_inverted_index.motifs | 157, 217 |
| abstract_inverted_index.neural | 1, 107, 275 |
| abstract_inverted_index.space. | 162, 199 |
| abstract_inverted_index.spaces | 139 |
| abstract_inverted_index.strong | 50, 232 |
| abstract_inverted_index.tasks. | 126, 240 |
| abstract_inverted_index.(DPP-A) | 186 |
| abstract_inverted_index.Second, | 163 |
| abstract_inverted_index.achieve | 110, 231 |
| abstract_inverted_index.analogy | 237 |
| abstract_inverted_index.complex | 26 |
| abstract_inverted_index.concept | 118 |
| abstract_inverted_index.ensures | 191 |
| abstract_inverted_index.exploit | 214 |
| abstract_inverted_index.insight | 44 |
| abstract_inverted_index.maximum | 192 |
| abstract_inverted_index.outside | 74 |
| abstract_inverted_index.process | 179 |
| abstract_inverted_index.propose | 165 |
| abstract_inverted_index.provide | 43, 114 |
| abstract_inverted_index.relies. | 32 |
| abstract_inverted_index.However, | 33 |
| abstract_inverted_index.ability. | 95 |
| abstract_inverted_index.abstract | 149 |
| abstract_inverted_index.capable. | 58 |
| abstract_inverted_index.combines | 207 |
| abstract_inverted_index.cortex): | 148 |
| abstract_inverted_index.coverage | 196 |
| abstract_inverted_index.describe | 97 |
| abstract_inverted_index.examples | 71 |
| abstract_inverted_index.features | 105 |
| abstract_inverted_index.function | 205 |
| abstract_inverted_index.identify | 83 |
| abstract_inverted_index.networks | 2 |
| abstract_inverted_index.operates | 170 |
| abstract_inverted_index.problems | 28 |
| abstract_inverted_index.provides | 242 |
| abstract_inverted_index.standard | 208 |
| abstract_inverted_index.supports | 49 |
| abstract_inverted_index.training | 79 |
| abstract_inverted_index.two-part | 99 |
| abstract_inverted_index.algorithm | 100 |
| abstract_inverted_index.attention | 185 |
| abstract_inverted_index.cognitive | 125 |
| abstract_inverted_index.emulating | 8 |
| abstract_inverted_index.improving | 270 |
| abstract_inverted_index.mammalian | 135, 254 |
| abstract_inverted_index.mechanism | 168 |
| abstract_inverted_index.networks. | 276 |
| abstract_inverted_index.organized | 154 |
| abstract_inverted_index.potential | 267 |
| abstract_inverted_index.recurring | 156, 216 |
| abstract_inverted_index.specific | 104 |
| abstract_inverted_index.therefore | 40 |
| abstract_inverted_index.arithmetic | 239 |
| abstract_inverted_index.contribute | 92, 257 |
| abstract_inverted_index.entorhinal | 147 |
| abstract_inverted_index.evaluating | 120 |
| abstract_inverted_index.human-like | 9 |
| abstract_inverted_index.integrated | 226 |
| abstract_inverted_index.processing | 86 |
| abstract_inverted_index.properties | 84 |
| abstract_inverted_index.relational | 152 |
| abstract_inverted_index.represents | 137 |
| abstract_inverted_index.sparseness | 193 |
| abstract_inverted_index.structure, | 153 |
| abstract_inverted_index.successful | 67 |
| abstract_inverted_index.tremendous | 5 |
| abstract_inverted_index.artificial | 274 |
| abstract_inverted_index.attentional | 167 |
| abstract_inverted_index.challenging | 124 |
| abstract_inverted_index.computation | 108 |
| abstract_inverted_index.performance | 68, 121, 235 |
| abstract_inverted_index.capabilities | 272 |
| abstract_inverted_index.distribution | 76 |
| abstract_inverted_index.increasingly | 15 |
| abstract_inverted_index.performance, | 260 |
| abstract_inverted_index.architectures | 229 |
| abstract_inverted_index.computational | 27 |
| abstract_inverted_index.determinantal | 177 |
| abstract_inverted_index.intelligence, | 10 |
| abstract_inverted_index.understanding | 19 |
| abstract_inverted_index.generalization | 53, 65, 234, 259 |
| abstract_inverted_index.interpretation | 245 |
| abstract_inverted_index.task-optimized | 209 |
| abstract_inverted_index.transformation | 189 |
| abstract_inverted_index.generalization, | 112 |
| abstract_inverted_index.representations | 150 |
| abstract_inverted_index.representational | 161 |
| abstract_inverted_index.out-of-distribution | 63 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.73486502 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |