Recent years have seen a surge of interest in the field of explainable A...
Many works in explainable AI have focused on explaining black-box
classi...
Knowledge transfer between heterogeneous source and target networks and ...
As artificial intelligence and machine learning algorithms become
increa...
Contrastive explanations for understanding the behavior of black box mod...
Feature based local attribution methods are amongst the most prevalent i...
As artificial intelligence and machine learning algorithms make further
...
There has been recent interest in improving performance of simple models...
Explaining decisions of deep neural networks is a hot research topic wit...
Stochastic gradient descent (SGD), which dates back to the 1950s, is one...
In this paper, we propose a new method called ProfWeight for transferrin...
We propose a novel online algorithm for training deep feedforward neural...
In this paper we propose a novel method that provides contrastive
explan...
We provide a novel notion of what it means to be interpretable, looking ...
We provide a novel notion of what it means to be interpretable, looking ...
We consider new formulations and methods for sparse quantile regression ...