Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Sommer, Johanna"'
Autor:
Flöge, Klemens, Udayakumar, Srisruthi, Sommer, Johanna, Piraud, Marie, Kesselheim, Stefan, Fortuin, Vincent, Günneman, Stephan, van der Weg, Karel J, Gohlke, Holger, Bazarova, Alina, Merdivan, Erinc
Recent AI advances have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal AI for proteins that integrates structural, sequence, alignment, and binding
Externí odkaz:
http://arxiv.org/abs/2411.04863
We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclidean point clouds via synthetic conformer coordinates and learns the in
Externí odkaz:
http://arxiv.org/abs/2406.10513
Publikováno v:
International Conference on Machine Learning. 2024. Oral
Although recent advances in higher-order Graph Neural Networks (GNNs) improve the theoretical expressiveness and molecular property predictive performance, they often fall short of the empirical performance of models that explicitly use fragment info
Externí odkaz:
http://arxiv.org/abs/2406.08210
Autor:
Lips, Antonia, Sommer, Johanna
Bakgrund: Depressions- och ångestsyndrom är de vanligaste förekommande psykiska sjukdomarna och leder i stor utsträckning till sjukskrivningar. Patientgruppen utgör en stor del av samhället, samt är en vårdsökande grupp som kommer påträffa
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-58970
Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions. Most models for molecule generation rely on the decomposition of molecules into frequently occurring substructures (
Externí odkaz:
http://arxiv.org/abs/2305.19303
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules. Deep generative models have shown promisi
Externí odkaz:
http://arxiv.org/abs/2306.17246
Autor:
Biloš, Marin, Sommer, Johanna, Rangapuram, Syama Sundar, Januschowski, Tim, Günnemann, Stephan
Neural ordinary differential equations describe how values change in time. This is the reason why they gained importance in modeling sequential data, especially when the observations are made at irregular intervals. In this paper we propose an altern
Externí odkaz:
http://arxiv.org/abs/2110.13040
End-to-end (geometric) deep learning has seen first successes in approximating the solution of combinatorial optimization problems. However, generating data in the realm of NP-hard/-complete tasks brings practical and theoretical challenges, resultin
Externí odkaz:
http://arxiv.org/abs/2110.10942
In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH). We propose a novel variant of the SH algorithm (MeSH), that uses me
Externí odkaz:
http://arxiv.org/abs/1909.07218
Autor:
Gache, Pascal1 (AUTHOR) pascal.gache@gmail.com, Sommer, Johanna2 (AUTHOR)
Publikováno v:
Therapeutic Patient Education / Éducation Thérapeutique du Patient. 2023, Vol. 15 Issue 2, p1-5. 5p.
