research
∙
02/09/2023
GCI: A (G)raph (C)oncept (I)nterpretation Framework
Explainable AI (XAI) underwent a recent surge in research on concept ext...
share
research
∙
01/25/2023
Towards Robust Metrics for Concept Representation Evaluation
Recent work on interpretability has focused on concept-based explanation...
research
∙
11/14/2022
Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations
Recent work has suggested post-hoc explainers might be ineffective for d...
research
∙
07/27/2022
Encoding Concepts in Graph Neural Networks
The opaque reasoning of Graph Neural Networks induces a lack of human tr...
research
∙
07/25/2021
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
While graph neural networks (GNNs) have been shown to perform well on gr...
research
∙
07/15/2021
Algorithmic Concept-based Explainable Reasoning
Recent research on graph neural network (GNN) models successfully applie...
research
∙
04/19/2021
Manipulating SGD with Data Ordering Attacks
Machine learning is vulnerable to a wide variety of different attacks. I...
research
∙
04/18/2021
Failing Conceptually: Concept-Based Explanations of Dataset Shift
Despite their remarkable performance on a wide range of visual tasks, ma...
research
∙
04/14/2021
Is Disentanglement all you need? Comparing Concept-based Disentanglement Approaches
Concept-based explanations have emerged as a popular way of extracting h...
research
∙
12/13/2020
MEME: Generating RNN Model Explanations via Model Extraction
Recurrent Neural Networks (RNNs) have achieved remarkable performance on...
research
∙
10/25/2020
Now You See Me (CME): Concept-based Model Extraction
Deep Neural Networks (DNNs) have achieved remarkable performance on a ra...
research
∙
04/16/2020
