An Investigation of the Weight Space for Version Control of Neural Networks. - Konstantin Schürholt; Damian Borth (CoRR, 2020)

Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction. - Konstantin Schürholt; Dimche Kostadinov; Damian Borth (NeurIPS, 2021)

Predicting Dynamic Stability of Power Grids using Graph Neural Networks. - Christian Nauck; Michael Lindner; Konstantin Schürholt; Haoming Zhang; Paul Schultz; Jürgen Kurths; Ingrid Isenhardt; Frank Hellmann (CoRR, 2021)

Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights. - Konstantin Schürholt; Boris Knyazev; Xavier Giró-i-Nieto; Damian Borth (NeurIPS, 2022)

Model Zoos: A Dataset of Diverse Populations of Neural Network Models. - Konstantin Schürholt; Diyar Taskiran; Boris Knyazev; Xavier Giró-i-Nieto; Damian Borth (NeurIPS, 2022)

Dynamic stability of power grids - new datasets for Graph Neural Networks. - Christian Nauck; Michael Lindner; Konstantin Schürholt; Frank Hellmann (CoRR, 2022)

Hyper-Representations for Pre-Training and Transfer Learning. - Konstantin Schürholt; Boris Knyazev; Xavier Giró-i-Nieto; Damian Borth (CoRR, 2022)

Towards dynamic stability analysis of sustainable power grids using graph neural networks. - Christian Nauck; Michael Lindner; Konstantin Schürholt; Frank Hellmann (CoRR, 2022)

Eurosat Model Zoo: A Dataset and Benchmark on Populations of Neural Networks and Its Sparsified Model Twins. - Dominik Honegger; Konstantin Schürholt; Linus Scheibenreif; Damian Borth (IGARSS, 2023)

Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models. - Dominik Honegger; Konstantin Schürholt; Damian Borth (CoRR, 2023)

Towards Scalable and Versatile Weight Space Learning. - Konstantin Schürholt; Michael W. Mahoney; Damian Borth (ICML, 2024)

MD tree: a model-diagnostic tree grown on loss landscape. - Yefan Zhou; Jianlong Chen; Qinxue Cao; Konstantin Schürholt; Yaoqing Yang (ICML, 2024)

Dirac-Bianconi Graph Neural Networks - Enabling Non-Diffusive Long-Range Graph Predictions. - Christian Nauck; Rohan Gorantla; Michael Lindner; Konstantin Schürholt; Antonia S. J. S. Mey; Frank Hellmann (CoRR, 2024)

Hyper-Representations: Learning from Populations of Neural Networks. - Konstantin Schürholt (CoRR, 2024)

Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction. - Léo Meynent; Ivan Melev; Konstantin Schürholt; Göran Kauermann; Damian Borth (CoRR, 2025)

The Impact of Model Zoo Size and Composition on Weight Space Learning. - Damian Falk; Konstantin Schürholt; Damian Borth (CoRR, 2025)

A Model Zoo of Vision Transformers. - Damian Falk; Léo Meynent; Florence Pfammatter; Konstantin Schürholt; Damian Borth (CoRR, 2025)

A Model Zoo on Phase Transitions in Neural Networks. - Konstantin Schürholt; Léo Meynent; Yefan Zhou; Haiquan Lu; Yaoqing Yang; Damian Borth (CoRR, 2025)

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights. - Zhiyuan Liang; Dongwen Tang; Yuhao Zhou; Xuanlei Zhao; Mingjia Shi; Wangbo Zhao; Zekai Li; Peihao Wang; Konstantin Schürholt; Damian Borth; Michael M. Bronstein; Yang You 001; et al. (CoRR, 2025)