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| 001 | 276091 | ||
| 005 | 20250121165738.0 | ||
| 037 | _ | _ | |a DZNE-2025-00172 |
| 100 | 1 | _ | |a Lagemann, Kai |0 P:(DE-2719)9001044 |b 0 |e First author |u dzne |
| 111 | 2 | _ | |a 12th International Conference on Learning Representations |g ICLR 2024 |c Vienna |d 2024-05-07 - 2024-05-11 |w Austria |
| 245 | _ | _ | |a INVARIANCE-BASED LEARNING OF LATENT DYNAMICS |
| 260 | _ | _ | |c 2024 |
| 300 | _ | _ | |a 200372 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1737452533_5211 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a We propose a new model class aimed at predicting dynamical trajectories from highdimensional empirical data. This is done by combining variational autoencodersand (spatio-)temporal transformers within a framework designed to enforce certainscientifically-motivated invariances. The models allow inference of system behavior at any continuous time and generalization well beyond the data distributionsseen during training. Furthermore, the models do not require an explicit neuralODE formulation, making them efficient and highly scalable in practice. We studybehavior through simple theoretical analyses and extensive empirical experiments.The latter investigate the ability to predict the trajectories of complicated systemsbased on finite data and show that the proposed approaches can outperform existingneural-dynamical models. We study also more general inductive bias in the contextof transfer to data obtained under entirely novel system interventions. Overall, ourresults provide a new framework for efficiently learning complicated dynamics in adata-driven manner, with potential applications in a wide range of fields includingphysics, biology, and engineering. |
| 536 | _ | _ | |a 354 - Disease Prevention and Healthy Aging (POF4-354) |0 G:(DE-HGF)POF4-354 |c POF4-354 |f POF IV |x 0 |
| 700 | 1 | _ | |a Lagemann, Christian |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Mukherjee, Sach |0 P:(DE-2719)2811372 |b 2 |e Last author |u dzne |
| 856 | 4 | _ | |u https://openreview.net/forum?id=EWTFMkTdkT |
| 856 | 4 | _ | |u https://pub.dzne.de/record/276091/files/DZNE-2025-00172.pdf |
| 856 | 4 | _ | |u https://pub.dzne.de/record/276091/files/DZNE-2025-00172.pdf?subformat=pdfa |x pdfa |
| 909 | C | O | |o oai:pub.dzne.de:276091 |p VDB |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 0 |6 P:(DE-2719)9001044 |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 2 |6 P:(DE-2719)2811372 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Neurodegenerative Diseases |1 G:(DE-HGF)POF4-350 |0 G:(DE-HGF)POF4-354 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Disease Prevention and Healthy Aging |x 0 |
| 920 | 1 | _ | |0 I:(DE-2719)1013030 |k AG Mukherjee |l Statistics and Machine Learning |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-2719)1013030 |
| 980 | _ | _ | |a UNRESTRICTED |
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