23 citations · 61 across the 11 of their papers we have counts for
5 papers · 1 filter
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh +1
While graph neural networks (GNNs) have been shown to perform well on graph-based data from a variety of fields, they suffer from a lack of transparency and accountability, which h…
Algorithmic Concept-based Explainable Reasoning
Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan +2
Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems. This has numerous benefits, su…
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…
Manipulating SGD with Data Ordering Attacks
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan +4
Machine learning is vulnerable to a wide variety of attacks. It is now well understood that by changing the underlying data distribution, an adversary can poison the model trained…