Learning Predictive Categories Using Lifted Relational Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00302620" target="_blank" >RIV/68407700:21230/17:00302620 - isvavai.cz</a>

Result on the web

<a href="http://ilp16.doc.ic.ac.uk/" target="_blank" >http://ilp16.doc.ic.ac.uk/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-63342-8_9" target="_blank" >10.1007/978-3-319-63342-8_9</a>

Result language

angličtina

Original language name

Learning Predictive Categories Using Lifted Relational Neural Networks

Original language description

Lifted relational neural networks (LRNNs) are a flexible neuralsymbolic framework based on the idea of lifted modelling. In this paper we show how LRNNs can be easily used to declaratively specify and solve a learning problem in which latent categories of entities and properties need to be jointly induced.

Czech name

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Czech description

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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)

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)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-319-63341-1

ISSN

0302-9743

e-ISSN

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Number of pages

12

Pages from-to

108-119

Publisher name

Springer-Verlag

Place of publication

Berlin

Event location

London

Event date

Sep 4, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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