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Found 7 papers, 4 papers with code
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image Classifiers
Convolutional Neural Networks (CNNs) are nowadays the model of choice in Computer Vision, thanks to their ability to automatize the feature extraction process in visual tasks.
DINE: Dimensional Interpretability of Node Embeddings
Towards that, we first develop new metrics that measure the global interpretability of embedding vectors based on the marginal contribution of the embedding dimensions to predicting graph structure.
Evaluating Link Prediction Explanations for Graph Neural Networks
Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains.
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
Finally, we discuss how this approach can be further exploited in terms of explainability and adversarial robustness.
Fast and Effective GNN Training with Linearized Random Spanning Trees
We present a new effective and scalable framework for training GNNs in node classification tasks, based on the effective resistance, a powerful tool solidly rooted in graph theory.
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs
Decoupling the feature space (edges) from a desired high-level explanation language (such as motifs) is thus a major challenge towards developing actionable explanations for graph classification tasks.
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling
Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms.
