15 citations · 18 across the 7 of their papers we have counts for
7 papers
Relevant Walk Search for Explaining Graph Neural Networks
Ping Xiong, Thomas Schnake, Michael Gastegger +3
Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer-wise rel…
Efficient Higher-order Subgraph Attribution via Message Passing
Ping Xiong, Thomas Schnake, Grégoire Montavon +2
Explaining graph neural networks (GNNs) has become more and more important recently. Higher-order interpretation schemes, such as GNN-LRP (layer-wise relevance propagation for GNN)…
Domain-Specific Word Embeddings with Structure Prediction
Stephanie Brandl, David Lassner, Anne Baillot +1
Complementary to finding good general word embeddings, an important question for representation learning is to find dynamic word embeddings, e.g., across time or domain. Current me…
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks
Dennis Grinwald, Kirill Bykov, Shinichi Nakajima +1
Explainable Artificial Intelligence (XAI) aims to make learning machines less opaque, and offers researchers and practitioners various tools to reveal the decision-making strategie…
Explaining Bayesian Neural Networks
Kirill Bykov, Marina M. -C. Höhne, Adelaida Creosteanu +4
To advance the transparency of learning machines such as Deep Neural Networks (DNNs), the field of Explainable AI (XAI) was established to provide interpretations of DNNs' predicti…
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights
Kirill Bykov, Anna Hedström, Shinichi Nakajima +1
Many efforts have been made for revealing the decision-making process of black-box learning machines such as deep neural networks, resulting in useful local and global explanation…
Local Function Complexity for Active Learning via Mixture of Gaussian Processes
Danny Panknin, Stefan Chmiela, Klaus-Robert Müller +1
Inhomogeneities in real-world data, e.g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of chal…