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Found 13 papers, 9 papers with code
GCI: A (G)raph (C)oncept (I)nterpretation Framework
Explainable AI (XAI) underwent a recent surge in research on concept extraction, focusing on extracting human-interpretable concepts from Deep Neural Networks.
Explainable Artificial Intelligence (XAI)
Molecular Property Prediction
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Towards Robust Metrics for Concept Representation Evaluation
In this paper, we show that such metrics are not appropriate for concept learning and propose novel metrics for evaluating the purity of concept representations in both approaches.
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 detecting spurious correlations in Deep Neural Networks (DNNs).
Encoding Concepts in Graph Neural Networks
The opaque reasoning of Graph Neural Networks induces a lack of human trust.
On The Quality Assurance Of Concept-Based Representations
Recent work on Explainable AI has focused on concept-based explanations, where deep learning models are explained in terms of high-level units of information, referred to as concepts.
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
Motivated by the aim of providing global explanations, we adapt the well-known Automated Concept-based Explanation approach (Ghorbani et al., 2019) to GNN node and graph classification, and propose GCExplainer.
Algorithmic Concept-based Explainable Reasoning
Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems.
Manipulating SGD with Data Ordering Attacks
Machine learning is vulnerable to a wide variety of attacks.
Failing Conceptually: Concept-Based Explanations of Dataset Shift
Using two case studies (dSprites and 3dshapes), we demonstrate how CBSD can accurately detect underlying concepts that are affected by shifts and achieve higher detection accuracy compared to state-of-the-art shift detection methods.
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models.
MEME: Generating RNN Model Explanations via Model Extraction
Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks.
Now You See Me (CME): Concept-based Model Extraction
Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks.
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of methodologies that have been applied in a wide range of fields.
