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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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