3 papers
cs.LG2026
Disentangling Homophily and Rarity: Explaining Failure in Graph Neural Networks
Preben M. Ness, Fariz Ikhwantri, Dusica Marijan
Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare? Some existing work frames classification of such nodes as a subgroup ge…
cs.LG2023
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models
Preben M. Ness, Dusica Marijan, Sunanda Bose
Causal Neural Network models have shown high levels of robustness to adversarial attacks as well as an increased capacity for generalisation tasks such as few-shot learning and rar…
eess.AS2018
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
Qiujia Li, Preben Ness, Anton Ragni +1
The standard approach to mitigate errors made by an automatic speech recognition system is to use confidence scores associated with each predicted word. In the simplest case, these…