| 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 | 43* | 2021 |
| 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 | 33 | 2022 |
| 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 | 30 | 2022 |
| 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 | 23 | 2022 |
| 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 | 10 | 2023 |
| 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 | 10 | 2022 |
| 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 | 4 | 2022 |
| 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 | 4 | 2022 |
| 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 | 3 | 2023 |
| An Investigation of the Weight Space to Monitor the Training Progress of Neural Networks K Schürholt, D Borth arXiv preprint arXiv:2006.10424, 2020 | 3* | 2020 |
| Towards Scalable and Versatile Weight Space Learning K Schürholt, MW Mahoney, D Borth arXiv preprint arXiv:2406.09997, 2024 | 2 | 2024 |
| 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 | 2 | 2023 |
| Towards predicting dynamic stability of power grids with Graph Neural Networks C Nauck, M Lindner, K Schürholt, F Hellmann | 1 | 2023 |
| Hyper-Representations: Learning from Populations of Neural Networks K Schürholt arXiv preprint arXiv:2410.05107, 2024 | | 2024 |
| Dirac--Bianconi Graph Neural Networks--Enabling Non-Diffusive Long-Range Graph Predictions C Nauck, R Gorantla, M Lindner, K Schürholt, ASJS Mey, F Hellmann arXiv preprint arXiv:2407.12419, 2024 | | 2024 |
| MD tree: a model-diagnostic tree grown on loss landscape Y Zhou, J Chen, Q Cao, K Schürholt, Y Yang arXiv preprint arXiv:2406.16988, 2024 | | 2024 |
| 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 |
| Dirac--Bianconi Graph Neural Networks-Enabling long-range graph predictions C Nauck, R Gorantla, M Lindner, K Schürholt, ASJS Mey, F Hellmann ICML 2024 Workshop on Geometry-grounded Representation Learning and …, 0 | | |
| Deep Graph Predictions using Dirac-Bianconi Graph Neural Networks C Nauck, R Gorantla, M Lindner, K Schürholt, ASJS Mey, F Hellmann | | |
Damian BorthProfessor of Artificial Intelligence & Machine Learning, University of St. GallenVerified email at unisg.ch
