Learning relational dependency networks in hybrid domains

By Irma Ravkic, J. Ramon, Jesse Davis. 2015

25 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luciano Serafini
2. A.S. d'Avila Garcez
3. Ivan Donadello
4. L. C. Lamb
5. Michael Spranger
6. Son Dinh Tran
7. Marco Gori
8. Samy Badreddine

In Information Systems

1. Luciano Serafini
2. Ivan Donadello
3. A.S. d'Avila Garcez

In Molecular Biology

1. Luciano Serafini
2. Ivan Donadello
3. A.S. d'Avila Garcez

Citations in the corpus

• Logic Tensor Networks for Semantic Image Interpretation (2017, background): “, 8]. The area of Statistical Relational Learning (SRL), or Statistical Artificial Intelligence (StarAI), seeks to combine data-driven learning, in the presence of uncertainty, with symbolic knowledge [29, 2, 13, 7, 26, 23]. However, only very few SRL systems have been applied to SII tasks (c.f. Sect”
• Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation (2017, background): “These domains, usually called hybrid domains [20], are constituted by a relational structure (e.g., a graph, a database) on a set of objects, and by a set numerical/con-Permission tinuous attributes associated to objects.”