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Markus Frohmann
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2020 – today
- 2024
- [c4]Markus Frohmann, Carolin Holtermann, Shahed Masoudian, Anne Lauscher, Navid Rekabsaz:
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale. ACL (Findings) 2024: 11743-11776 - [c3]Carolin Holtermann, Markus Frohmann, Navid Rekabsaz, Anne Lauscher:
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition. EACL (Findings) 2024: 1138-1157 - [c2]Shahed Masoudian, Markus Frohmann, Navid Rekabsaz, Markus Schedl:
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization. EMNLP 2024: 10932-10938 - [c1]Markus Frohmann, Igor Sterner, Ivan Vulic, Benjamin Minixhofer, Markus Schedl:
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation. EMNLP 2024: 11908-11941 - [i4]Carolin Holtermann, Markus Frohmann, Navid Rekabsaz, Anne Lauscher:
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition. CoRR abs/2401.12756 (2024) - [i3]Markus Frohmann, Igor Sterner, Ivan Vulic, Benjamin Minixhofer, Markus Schedl:
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation. CoRR abs/2406.16678 (2024) - [i2]Shahed Masoudian, Markus Frohmann, Navid Rekabsaz, Markus Schedl:
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization. CoRR abs/2409.19541 (2024) - 2023
- [j1]Markus Frohmann
, Manuel Karner, Said Khudoyan, Robert Wagner, Markus Schedl
Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting. Big Data Cogn. Comput. 7(3): 137 (2023) - [i1]Markus Frohmann, Carolin Holtermann, Shahed Masoudian, Anne Lauscher, Navid Rekabsaz:
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale. CoRR abs/2310.01217 (2023)
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