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Found 11 papers, 2 papers with code
Knowledge Refactoring for Inductive Program Synthesis
We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it.
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
This paper studies semi-supervised clustering in the context of time series.
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
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.
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation
The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier.
An expressive dissimilarity measure for relational clustering using neighbourhood trees
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.
Theory reconstruction: a representation learning view on predicate invention
With this positional paper we present a representation learning view on predicate invention.
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs.
Learning Relational Representations with Auto-encoding Logic Programs
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.
Feature Interactions in XGBoost
Further, the improvement in the model structure can also lead to better interpretability.
SaDe: Learning Models that Provably Satisfy Domain Constraints
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).
DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction
In this paper, we present an approach to train neural networks which can enforce a wide variety of constraints and guarantee that the constraint is satisfied by all possible predictions.
