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…
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…
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
Xujiang Zhao, Yuzhe Ou, Lance Kaplan +2
Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of…
Evidential Deep Learning to Quantify Classification Uncertainty
Murat Sensoy, Lance Kaplan, Melih Kandemir
Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to m…