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Konstantin Schürholt
Konstantin Schürholt
Researcher & PhD Candidate, AIML Lab, University of St.Gallen
Verified email at unisg.ch - Homepage
Title
Cited by
Cited by
Year
Predicting basin stability of power grids using graph neural networks
C Nauck, M Lindner, K Schürholt, H Zhang, P Schultz, J Kurths, I Isenhardt, ...
New Journal of Physics 24 (4), 043041, 2022
232022
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
K Schürholt, D Kostadinov, D Borth
Conference on Neural Information Processing Systems (NeurIPS), 2021, 2021
22*2021
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
K Schürholt, B Knyazev, X Giró-i-Nieto, D Borth
Conference on Neural Information Processing Systems (NeurIPS), 2022, 2022
152022
Model Zoo: A Dataset of Diverse Populations of Neural Network Models
K Schürholt, D Taskiran, B Knyazev, X Giró-i-Nieto, D Borth
Conference on Neural Information Processing Systems (NeurIPS), 2022, 2022
142022
Hyper-Representations for Pre-Training and Transfer Learning
K Schürholt, B Knyazev, X Giró-i-Nieto, D Borth
ICML 2022 Workshop of Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022
72022
Toward dynamic stability assessment of power grid topologies using graph neural networks
C Nauck, M Lindner, K Schürholt, F Hellmann
Chaos: An Interdisciplinary Journal of Nonlinear Science 33 (10), 2023
52023
Elements of future snowpack modeling–Part 1: A physical instability arising from the nonlinear coupling of transport and phase changes
K Schürholt, J Kowalski, H Löwe
The Cryosphere 16 (3), 903-923, 2022
42022
Towards dynamic stability analysis of sustainable power grids using graph neural networks
C Nauck, M Lindner, K Schürholt, F Hellmann
NeurIPS 2022 Climate Change AI Workshop, 2022
22022
An Investigation of the Weight Space to Monitor the Training Progress of Neural Networks
K Schürholt, D Borth
arXiv preprint arXiv:2006.10424, 2020
2*2020
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
D Honegger, K Schürholt, D Borth
ICLR 2023 Workshop on Sparsity in Neural Networks, 2023
12023
Deep Graph Predictions using Dirac-Bianconi Graph Neural Networks
C Nauck, R Gorantla, M Lindner, K Schürholt, ASJS Mey, F Hellmann
2023
Eurosat Model Zoo: A Dataset and Benchmark on Populations of Neural Networks and Its Sparsified Model Twins
D Honegger, K Schürholt, L Scheibenreif, D Borth
IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium …, 2023
2023
Towards predicting dynamic stability of power grids with Graph Neural Networks
C Nauck, M Lindner, K Schürholt, F Hellmann
2022
Dynamic stability of power grids-new datasets for Graph Neural Networks.
C Nauck, M Lindner, K Schürholt, F Hellmann
CoRR, 2022
2022
On water vapor transport in snowpack models: Comparison of existing schemes, numerical requirements and the role of non-local advection.
K Schürholt, J Kowalski, H Löwe
Geophysical Research Abstracts 21, 2019
2019
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