Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00374040" target="_blank" >RIV/68407700:21230/24:00374040 - isvavai.cz</a>

Výsledek na webu

<a href="https://openreview.net/forum?id=mF3cTns4pe" target="_blank" >https://openreview.net/forum?id=mF3cTns4pe</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs

Popis výsledku v původním jazyce

Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models utilize neural networks to learn a probability distribution over undirected cyclic graphs. This assumption of a generic graph structure brings various computational challenges, and, more importantly, the presence of non-linearities in neural networks does not permit tractable probabilistic inference. We address these problems by proposing sum-product-set networks, an extension of probabilistic circuits from unstructured tensor data to tree-structured graph data. To this end, we use random finite sets to reflect a variable number of nodes and edges in the graph and to allow for exact and efficient inference. We demonstrate that our tractable model performs comparably to various intractable models based on neural networks.

Název v anglickém jazyce

Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs

Popis výsledku anglicky

Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models utilize neural networks to learn a probability distribution over undirected cyclic graphs. This assumption of a generic graph structure brings various computational challenges, and, more importantly, the presence of non-linearities in neural networks does not permit tractable probabilistic inference. We address these problems by proposing sum-product-set networks, an extension of probabilistic circuits from unstructured tensor data to tree-structured graph data. To this end, we use random finite sets to reflect a variable number of nodes and edges in the graph and to allow for exact and efficient inference. We demonstrate that our tractable model performs comparably to various intractable models based on neural networks.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA22-32620S" target="_blank" >GA22-32620S: Učení bez učitele nad heterogenními strukturovanými daty</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceeding The Twelfth International Conference on Learning Representations (ICLR 2024)

ISBN

9781713898658

ISSN

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e-ISSN

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Počet stran výsledku

30

Strana od-do

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Název nakladatele

International Conference on Learning Representations

Místo vydání

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Místo konání akce

Vídeň

Datum konání akce

7. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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