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Found 7 papers, 4 papers with code
Building and Interpreting Deep Similarity Models
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'.
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i. e. by identifying groups of edges that jointly contribute to the prediction.
XAI for Transformers: Better Explanations through Conservative Propagation
Transformers have become an important workhorse of machine learning, with numerous applications.
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?
We investigate whether self-attention in large-scale pre-trained language models is as predictive of human eye fixation patterns during task-reading as classical cognitive models of human attention.
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
An ML based analysis of these tables helps to unveil important facets of the spatio-temporal evolution of knowledge and innovation in the field of mathematical astronomy in the period, as taught at European universities.
Rather a Nurse than a Physician -- Contrastive Explanations under Investigation
A cross-comparison between model-based rationales and human annotations, both in contrastive and non-contrastive settings, yields a high agreement between the two settings for models as well as for humans.
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations
Rationales in the form of manually annotated input spans usually serve as ground truth when evaluating explainability methods in NLP.
