3 citations
6 papers
Robots Need Some Education: On the complexity of learning in evolutionary robotics
Fuda van Diggelen
Evolutionary Robotics and Robot Learning are two fields in robotics that aim to automatically optimize robot designs. The key difference between them lies in what is being optimize…
Hierarchical Conformal Classification
Floris den Hengst, Inès Blin, Majid Mohammadi +2
Conformal prediction (CP) is a powerful framework for quantifying uncertainty in machine learning models, offering reliable predictions with finite-sample coverage guarantees. When…
SUBMASSIVE: Resolving Subclass Cycles in Very Large Knowledge Graphs
Shuai Wang, Peter Bloem, Joe Raad +1
Large knowledge graphs capture information of a large number of entities and their relations. Among the many relations they capture, class subsumption assertions are usually presen…
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
David W. Romero
The emergence of Deep Learning has marked a profound shift in machine learning, driven by numerous breakthroughs achieved in recent years. However, as Deep Learning becomes increas…
Optimisation in Neurosymbolic Learning Systems
Emile van Krieken
Neurosymbolic AI aims to integrate deep learning with symbolic AI. This integration has many promises, such as decreasing the amount of data required to train a neural network, imp…
False Positive and Cross-relation Signals in Distant Supervision Data
Anca Dumitrache, Lora Aroyo, Chris Welty
Distant supervision (DS) is a well-established method for relation extraction from text, based on the assumption that when a knowledge-base contains a relation between a term pair,…