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Anna Krause
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2020 – today
- 2024
- [c14]Julian Tritscher, Maximilian Wolf, Anna Krause, Andreas Hotho, Daniel Schlör:
Data Generation for Explainable Occupational Fraud Detection. KI 2024: 246-259 - [c13]Andrzej Dulny, Paul Heinisch, Andreas Hotho, Anna Krause:
GrINd: Grid Interpolation Network for Scattered Observations. ECML/PKDD (7) 2024: 177-193 - [c12]Julian Tritscher, Philip Lissmann, Maximilian Wolf, Anna Krause, Andreas Hotho, Daniel Schlör:
Generative Inpainting for Shapley-Value-Based Anomaly Explanation. xAI (1) 2024: 230-243 - [i10]Pascal Janetzky, Florian Gallusser, Simon Hentschel, Andreas Hotho, Anna Krause:
Global Vegetation Modeling with Pre-Trained Weather Transformers. CoRR abs/2403.18438 (2024) - [i9]Andrzej Dulny, Paul Heinisch, Andreas Hotho, Anna Krause:
GrINd: Grid Interpolation Network for Scattered Observations. CoRR abs/2403.19570 (2024) - 2023
- [j3]Michael Steininger
, Daniel Abel
ConvMOS: climate model output statistics with deep learning. Data Min. Knowl. Discov. 37(1): 136-166 (2023) - [j2]Julian Tritscher, Anna Krause, Andreas Hotho:
Feature relevance XAI in anomaly detection: Reviewing approaches and challenges. Frontiers Artif. Intell. 6 (2023) - [c11]Pascal Janetzky, Melanie Schaller, Anna Krause, Andreas Hotho:
Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data. IWSSIP 2023: 1-5 - [c10]Pascal Janetzky, Philip Lissmann, Andreas Hotho, Anna Krause:
Automatic Speech Detection on a Smart Beehive's Raspberry Pi. LWDA 2023: 424-429 - [c9]Andrzej Dulny
DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data. ECML/PKDD (1) 2023: 438-455 - [i8]Andrzej Dulny, Andreas Hotho, Anna Krause:
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data. CoRR abs/2306.05805 (2023) - [i7]Paul Heinisch, Andrzej Dulny, Anna Krause, Andreas Hotho:
TaylorPDENet: Learning PDEs from non-grid Data. CoRR abs/2306.14511 (2023) - 2022
- [c8]Andrzej Dulny, Andreas Hotho, Anna Krause:
NeuralPDE: Modelling Dynamical Systems from Data. KI 2022: 75-89 - [c7]Julian Tritscher, Daniel Schlör, Fabian Gwinner, Anna Krause, Andreas Hotho:
Towards Explainable Occupational Fraud Detection. PKDD/ECML Workshops (2) 2022: 79-96 - [i6]Julian Tritscher, Fabian Gwinner, Daniel Schlör, Anna Krause, Andreas Hotho:
Open ERP System Data For Occupational Fraud Detection. CoRR abs/2206.04460 (2022) - [i5]Padraig Davidson, Michael Steininger, André Huhn, Anna Krause, Andreas Hotho:
Semi-unsupervised Learning for Time Series Classification. CoRR abs/2207.03119 (2022) - 2021
- [j1]Michael Steininger
Density-based weighting for imbalanced regression. Mach. Learn. 110(8): 2187-2211 (2021) - [c6]Julian Tritscher, Anna Krause, Daniel Schlör, Fabian Gwinner, Sebastian von Mammen, Andreas Hotho:
A financial game with opportunities for fraud. CoG 2021: 1-5 - [c5]Pascal Janetzky, Padraig Davidson, Michael Steininger, Anna Krause, Andreas Hotho:
Detecting Presence Of Speech In Acoustic Data Obtained From Beehives. DCASE 2021: 26-30 - [c4]Padraig Davidson, Florian Buckermann, Michael Steininger, Anna Krause, Andreas Hotho:
Semi-unsupervised Learning: An In-depth Parameter Analysis. KI 2021: 51-66 - [i4]Padraig Davidson, Michael Steininger, Florian Lautenschlager, Anna Krause, Andreas Hotho:
Anomaly Detection in Beehives: An Algorithm Comparison. CoRR abs/2110.03945 (2021) - [i3]Andrzej Dulny, Andreas Hotho, Anna Krause:
NeuralPDE: Modelling Dynamical Systems from Data. CoRR abs/2111.07671 (2021) - 2020
- [c3]Konstantin Kobs, Christian Schäfer
Semi-Supervised Learning for Grain Size Distribution Interpolation. ICPR Workshops (6) 2020: 34-44 - [c2]Daniel Schlör, Markus Ring, Anna Krause, Andreas Hotho:
Financial Fraud Detection with Improved Neural Arithmetic Logic Units. MIDAS@PKDD/ECML 2020: 40-54 - [c1]Padraig Davidson, Michael Steininger, Florian Lautenschlager, Konstantin Kobs, Anna Krause, Andreas Hotho:
Anomaly Detection in Beehives using Deep Recurrent Autoencoders. SENSORNETS 2020: 142-149 - [i2]Padraig Davidson, Michael Steininger, Florian Lautenschlager, Konstantin Kobs, Anna Krause
Anomaly Detection in Beehives using Deep Recurrent Autoencoders. CoRR abs/2003.04576 (2020) - [i1]Michael Steininger, Daniel Abel, Katrin Ziegler, Anna Krause, Heiko Paeth, Andreas Hotho:
Deep Learning for Climate Model Output Statistics. CoRR abs/2012.10394 (2020)
2010 – 2019
- 2019
- [b1]Anna Krause:
Variation-aware behavioural modelling using support vector machines and affine arithmetic. University of Hanover, Hannover, Germany, 2019
Coauthor Index
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last updated on 2024-10-22 21:13 CEST by the dblp team
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