In this work, we represent product concepts using database queries and tackle two learning problems.
A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class.
In many real world applications of machine learning, models have to meet certain domain-based requirements that can be expressed as constraints (e. g., safety-critical constraints in autonomous driving systems).
This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks.
This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not?
Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce.
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.
The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier.
With this positional paper we present a representation learning view on predicate invention.
It is the first measure to incorporate a wide variety of types of similarity, including similarity of attributes, similarity of relational context, and proximity in a hypergraph.
The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language.
This paper provides a gentle introduction to problem solving with the IDP3 system.