Demystifying Relational Latent Representations

By Sebastijan Dumancic, Hendrik Blockeel. 2017

7 citations recorded by the source corpus.

Latent features learned by deep learning approaches have proven to be a powerful tool for machine learning. They serve as a data abstraction that makes learning easier by capturing regularities in data explicitly. Their benefits motivated their adaptation to the relational learning context. In our previous work, we introduce an approach that learns relational latent features by means of clustering instances and their relations. The major drawback of latent representations is that they are often black-box and difficult to interpret. This work addresses these issues and shows that (1) latent features created by clustering are interpretable and capture interesting properties of data; (2) they identify local regions of instances that match well with the label, which partially explains their benefit; and (3) although the number of latent features generated by this approach is large, often many of them are highly redundant and can be removed without hurting performance much.

Subfields: Artificial Intelligence, Management Science and Operations Research

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. Ugur Kursuncu
2. Manas Gaur
3. Amit P. Sheth

In Management Science and Operations Research

1. Ugur Kursuncu
2. Manas Gaur
3. Amit P. Sheth

Citations in the corpus

• Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “However, this operation cannot be done arbitrarily as the vector spaces of both representations are different both in dimension and distribution if not same (Dumančić and Blockeel 2017).”