Deep Learning with Relational Logic Representations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00333120" target="_blank" >RIV/68407700:21230/19:00333120 - isvavai.cz</a>
Result on the web
<a href="https://www.ijcai.org/proceedings/2019/920" target="_blank" >https://www.ijcai.org/proceedings/2019/920</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.24963/ijcai.2019/920" target="_blank" >10.24963/ijcai.2019/920</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning with Relational Logic Representations
Original language description
Despite their significant success, all the existing deep neural architectures based on static computational graphs processing fixed tensor representations necessarily face fundamental limitations when presented with dynamically sized and structured data. Examples of these are sparse multi-relational structures present everywhere from biological networks and complex knowledge hyper-graphs to logical theories. Likewise, given the cryptic nature of generalization and representation learning in neural networks, potential integration with the sheer amounts of existing symbolic abstractions present in human knowledge remains highly problematic. Here, we argue that these abilities, naturally present in symbolic approaches based on the expressive power of relational logic, are necessary to be adopted for further progress of neural networks, and present a well founded learning framework for integration of deep and symbolic approaches based on the lifted modelling paradigm.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-26999S" target="_blank" >GA17-26999S: Deep relational learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
ISBN
978-0-9992411-4-1
ISSN
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e-ISSN
1045-0823
Number of pages
2
Pages from-to
6462-6463
Publisher name
International Joint Conferences on Artificial Intelligence Organization
Place of publication
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Event location
Macau
Event date
Aug 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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