1 code implementation • 29 Feb 2024 • Stephanie Brandl, Oliver Eberle, Tiago Ribeiro, Anders Søgaard, Nora Hollenstein
Rationales in the form of manually annotated input spans usually serve as ground truth when evaluating explainability methods in NLP.
no code implementations • 18 Oct 2023 • Oliver Eberle, Ilias Chalkidis, Laura Cabello, Stephanie Brandl
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.
no code implementations • 13 Oct 2023 • Oliver Eberle, Jochen Büttner, Hassan El-Hajj, Grégoire Montavon, Klaus-Robert Müller, Matteo Valleriani
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.
1 code implementation • ACL 2022 • Stephanie Brandl, Oliver Eberle, Jonas Pilot, Anders Søgaard
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.
1 code implementation • 15 Feb 2022 • Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf
Transformers have become an important workhorse of machine learning, with numerous applications.
no code implementations • 5 Jun 2020 • Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
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.
1 code implementation • 11 Mar 2020 • Oliver Eberle, Jochen Büttner, Florian Kräutli, Klaus-Robert Müller, Matteo Valleriani, Grégoire Montavon
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'.