23 citations · 29 across the 6 of their papers we have counts for
6 papers
GCI: A (G)raph (C)oncept (I)nterpretation Framework
Dmitry Kazhdan, Botty Dimanov, Lucie Charlotte Magister +3
Explainable AI (XAI) underwent a recent surge in research on concept extraction, focusing on extracting human-interpretable concepts from Deep Neural Networks. An important challen…
Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations
Shea Cardozo, Gabriel Islas Montero, Dmitry Kazhdan +4
Recent work has suggested post-hoc explainers might be ineffective for detecting spurious correlations in Deep Neural Networks (DNNs). However, we show there are serious weaknesses…
Failing Conceptually: Concept-Based Explanations of Dataset Shift
Maleakhi A. Wijaya, Dmitry Kazhdan, Botty Dimanov +1
Despite their remarkable performance on a wide range of visual tasks, machine learning technologies often succumb to data distribution shifts. Consequently, a range of recent work…
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Dmitry Kazhdan, Botty Dimanov, Helena Andres Terre +3
Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models. At the same time, the disentanglement le…
MEME: Generating RNN Model Explanations via Model Extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik +1
Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering RNN-based approaches is improving their explainability a…
Now You See Me (CME): Concept-based Model Extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik +2
Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In th…