Matthias Aßenmacher

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 21

Top Topics

Active Learning

Constrained Clustering

Unconstrained Handwritten

Top Venues

Publications

Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting
Divergent Token Metrics: Measuring degradation to prune away LLM components - and optimize quantization. NAACL-HLT (2024)
Esteban Garces Arias, Julian Rodemann, Meimingwei Li, Christian Heumann, Matthias Aßenmacher
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation. CoRR (2024)
Cem Akkus, Luyang Chu, Vladana Djakovic, Steffen Jauch-Walser, Philipp Koch, Giacomo Loss, Christopher Marquardt, Marco Moldovan, Nadja Sauter, Maximilian Schneider, Rickmer Schulte, Karol Urbanczyk, Jann Goschenhofer, Christian Heumann, Rasmus Hvingelby, Daniel Schalk, Matthias Aßenmacher
Multimodal Deep Learning. CoRR (2023)
Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher, Christian Heumann, Stephanie Thiemichen
How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses. CoRR (2023)
Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting
Divergent Token Metrics: Measuring degradation to prune away LLM components - and optimize quantization. CoRR (2023)
Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann, Esteban Garces Arias, Marius Roger, Bernd Bischl, Matthias Aßenmacher
How Different Is Stereotypical Bias Across Languages? CoRR (2023)
Philipp Koch, Gilary Vera Nuñez, Esteban Garces Arias, Christian Heumann, Matthias Schöffel, Alexander Häberlin, Matthias Aßenmacher
A tailored Handwritten-Text-Recognition System for Medieval Latin. ALP@RANLP (2023)
Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. CoRR (2023)
Philipp Koch, Gilary Vera Nuñez, Esteban Garces Arias, Christian Heumann, Matthias Schöffel, Alexander Häberlin, Matthias Aßenmacher
A tailored Handwritten-Text-Recognition System for Medieval Latin. CoRR (2023)
Matthias Aßenmacher, Nadja Sauter, Christian Heumann
Classifying multilingual party manifestos: Domain transfer across country, time, and genre. CoRR (2023)
Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. ECML/PKDD (1) (2023)
Matthias Aßenmacher, Lukas Rauch, Jann Goschenhofer, Andreas Stephan, Bernd Bischl, Benjamin Roth, Bernhard Sick
Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. IAL@PKDD/ECML (2023)
Elisabeth Lebmeier, Matthias Aßenmacher, Christian Heumann
On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based Sentiment Analysis. ECML/PKDD (2) (2022)
Philipp Koch, Matthias Aßenmacher, Christian Heumann
Pre-trained language models evaluating themselves - A comparative study. Insights@ACL (2022)
Maximilian Meidinger, Matthias Aßenmacher
A New Benchmark for NLP in Social Sciences: Evaluating the Usefulness of Pre-trained Language Models for Classifying Open-ended Survey Responses. ICAART (2) (2021)
Matthias Aßenmacher, Alessandra Corvonato, Christian Heumann
Re-Evaluating GermEval17 Using German Pre-Trained Language Models. CoRR (2021)
Matthias Aßenmacher, Alessandra Corvonato, Christian Heumann
Re-Evaluating GermEval17 Using German Pre-Trained Language Models. SwissText (2021)
Patrick Schulze, Simon Wiegrebe, Paul W. Thurner, Christian Heumann, Matthias Aßenmacher, Sandra Wankmüller
Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach. CoRR (2021)
Matthias Aßenmacher, Christian Heumann
On the Comparability of Pre-trained Language Models. SwissText/KONVENS (2020)
Matthias Aßenmacher, Christian Heumann
On the comparability of Pre-trained Language Models. CoRR (2020)
Maike Guderlei, Matthias Aßenmacher
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News. COLING (2020)