guest ::
| Home > Publications database > Nonlinear Isometric Manifold Learning for Injective Normalizing Flows > print |
| Home > Publications database > Nonlinear Isometric Manifold Learning for Injective Normalizing Flows > print |
| 001 | 917558 | ||
| 005 | 20240712112854.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2203.03934 |2 doi |
| 024 | 7 | _ | |a 2128/33644 |2 Handle |
| 037 | _ | _ | |a FZJ-2023-00760 |
| 100 | 1 | _ | |a Cramer, Eike |0 P:(DE-Juel1)179591 |b 0 |u fzj |
| 245 | _ | _ | |a Nonlinear Isometric Manifold Learning for Injective Normalizing Flows |
| 260 | _ | _ | |c 2022 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1673946290_26809 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a To model manifold data using normalizing flows, we propose to employ the isometric autoencoder to design nonlinear encodings with explicit inverses. The isometry allows us to separate manifold learning and density estimation and train both parts to high accuracy. Applied to the MNIST data set, the combined approach generates high-quality images. |
| 536 | _ | _ | |a 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112) |0 G:(DE-HGF)POF4-1121 |c POF4-112 |f POF IV |x 0 |
| 536 | _ | _ | |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) |0 G:(DE-Juel1)HDS-LEE-20190612 |c HDS-LEE-20190612 |x 1 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Rauh, Felix |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 2 |u fzj |
| 700 | 1 | _ | |a Tempone, Raúl |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Dahmen, Manuel |0 P:(DE-Juel1)172097 |b 4 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2203.03934 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/917558/files/2203.03934.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:917558 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)179591 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 0 |6 P:(DE-Juel1)179591 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 1 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)172025 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 2 |6 P:(DE-Juel1)172025 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 3 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)172097 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Energie |l Energiesystemdesign (ESD) |1 G:(DE-HGF)POF4-110 |0 G:(DE-HGF)POF4-112 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-100 |4 G:(DE-HGF)POF |v Digitalisierung und Systemtechnik |9 G:(DE-HGF)POF4-1121 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IEK-10-20170217 |k IEK-10 |l Modellierung von Energiesystemen |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IEK-10-20170217 |
| 981 | _ | _ | |a I:(DE-Juel1)ICE-1-20170217 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|