Stacked Structure Learning for Lifted Relational Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00320432" target="_blank" >RIV/68407700:21230/18:00320432 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-78090-0_10" target="_blank" >http://dx.doi.org/10.1007/978-3-319-78090-0_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-78090-0_10" target="_blank" >10.1007/978-3-319-78090-0_10</a>
Alternative languages
Result language
angličtina
Original language name
Stacked Structure Learning for Lifted Relational Neural Networks
Original language description
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While previous work has shown that using LRNNs can lead to state-of-the-art results in various ILP tasks, these results depended on hand-crafted rules. In this paper, we extend the framework of LRNNs with structure learning, thus enabling a fully automated learning process. Similarly to many ILP methods, our structure learning algorithm proceeds in an iterative fashion by top-down searching through the hypothesis space of all possible Horn clauses, considering the predicates that occur in the training examples as well as invented soft concepts entailed by the best weighted rules found so far. In the experiments, we demonstrate the ability to automatically induce useful hierarchical soft concepts leading to deep LRNNs with a competitive predictive power.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Inductive Logic Programming 2017
ISBN
978-3-319-78089-4
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
140-151
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Orléans
Event date
Sep 4, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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