Subdirect representations of relational systems

Demystifying Relational Latent Representations

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 dataion that makes learning easier by capturing regularities in data explicitly. Their benefits motivated their adaptation to relational learning context. In our previous work, we introduce an approac...

Subdirect Sums of Rings

Fuzzy Relational Matrix-Based Stability Analysis for First-Order Fuzzy Relational Dynamic Systems

In this paper, two sets of sufficient conditions are obtained to ensure the existence and stability of a unique equilibrium point of unforced first-order fuzzy relational dynamical systems by using two different approaches which are both based on the fuzzy relational matrix of the model.In the first approach, the equilibrium point of the system is one of the centers of the related membership fu...

Learning Distributed Representations of Relational Data Using Linear Relational Embedding

I shall discuss new methods for solving the problem of generalizing from relational data. I consider a situation in which we have a set of concepts and a set of relations among these concepts, and the data consists of few instances of these relations that hold among the concepts; the aim is to infer other instances of these relations. My approach is to learn from the data a representation of th...

Relational Representations that Facilitate Learning