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ORCIDSandra Gilhuber
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- affiliation: LMU Munich, Institute of Informatics, Germany
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2020 – today
- 2024
[c8]Sandra Gilhuber, Anna Beer, Yunpu Ma, Thomas Seidl:
FALCUN: A Simple and Efficient Deep Active Learning Strategy. ECML/PKDD (3) 2024: 421-439- [i5]Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier:
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. CoRR abs/2406.17322 (2024) - 2023
- [c7]Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. ECML/PKDD (1) 2023: 75-91 - [c6]Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl:
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. ECML/PKDD (2) 2023: 330-347 - [i4]Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. CoRR abs/2308.00146 (2023) - [i3]Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl:
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning. CoRR abs/2308.08224 (2023) - 2022
- [c5]Sandra Gilhuber, Philipp Jahn
, Yunpu Ma, Thomas Seidl:
VERIPS: Verified Pseudo-label Selection for Deep Active Learning. ICDM 2022: 951-956 - [c4]Sandra Gilhuber, Max Berrendorf, Yunpu Ma, Thomas Seidl:
Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations. IAL@PKDD/ECML 2022: 43-48 - 2021
- [c3]Sandra Obermeier, Anna Beer, Florian Wahl, Thomas Seidl:
Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering. BTW 2021: 175-194 - [c2]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. ECIR (2) 2021: 264-271 - [e1]Thomas Seidl, Michael Fromm, Sandra Obermeier:
Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. CEUR Workshop Proceedings 2993, CEUR-WS.org 2021 [contents] - 2020
- [c1]Sandra Obermeier, Max Berrendorf, Peer Kröger:
Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation. WI/IAT 2020: 387-390 - [i2]Sandra Obermeier, Max Berrendorf, Peer Kröger:
Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation. CoRR abs/2011.01773 (2020) - [i1]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. CoRR abs/2011.02177 (2020)
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