52 citations · 92 across the 8 of their papers we have counts for
4 papers · 1 filter
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Daniel Cunnington, Alessandra Russo, Mark Law +2
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems re…
A General Framework for Distributed Inference with Uncertain Models
James Z. Hare, Cesar A. Uribe, Lance Kaplan +1
This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best descr…
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
Marc Roig Vilamala, Harrison Taylor, Tianwei Xing +6
Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is a…
Uncertainty-Aware Deep Classifiers using Generative Models
Murat Sensoy, Lance Kaplan, Federico Cerutti +1
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertai…