544 citations · 617 across the 22 of their papers we have counts for
24 papers
Targeted Visual Counterfactual Explanations for Contrastive Vision-Language Model
Van Bach Nguyen, Jörg Schlötterer, Christin Seifer
Current explanation methods for contrastive vision-language models such as CLIP mainly identify important regions without showing how to change the input in order to get a target p…
XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders
Khawla Elhadri, Jörg Schlötterer, Christin Seifert
In data-driven applications relying on tabular data, where interpretability is key, machine learning models such as decision trees and linear regression are applied. Although neura…
Towards Interpretable Deep Neural Networks for Tabular Data
Khawla Elhadri, Jörg Schlötterer, Christin Seifert
Tabular data is the foundation of many applications in fields such as finance and healthcare. Although DNNs tailored for tabular data achieve competitive predictive performance, th…
Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support
Jan Trienes, Anastasiia Derzhanskaia, Roland Schwarzkopf +3
We present Marcel, a lightweight and open-source conversational agent designed to support prospective students with admission-related inquiries. The system aims to provide fast and…
Invariant Learning with Annotation-free Environments
Phuong Quynh Le, Christin Seifert, Jörg Schlötterer
Invariant learning is a promising approach to improve domain generalization compared to Empirical Risk Minimization (ERM). However, most invariant learning methods rely on the assu…
Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
Van Bach Nguyen, Christin Seifert, Jörg Schlötterer
The need for interpretability in deep learning has driven interest in counterfactual explanations, which identify minimal changes to an instance that change a model's prediction. C…