Good Papers

Showing papers from Forschungszentrum Juelich GmbH Show all papers

91%Must read
?Must readVote to see the score
NeurIPS 2026Technical University of MunichForschungszentrum Juelich GmbHTechnical University of Munich /Physics-informed ML & PDEs

Transferable SCF-Acceleration through Solver-Aligned Initialization Learning

Solver-Aligned Initialization Learning differentiates through SCF solvers to train transferable ML initial guesses, reducing iterations by up to 37% on molecules up to 10× larger than training data.

Eike S. Eberhard, Viktor Kotsev, Timm Güthle, Stephan Günnemann

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
18/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 18 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 4/5
76%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026FZJ & RWTH AachenForschungszentrum JülichForschungszentrum Juelich GmbHRheinisch Westfälische TechnischRWTH AachenUnified multimodal models

Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG

Brain-OF is a multimodal foundation model jointly pretrained on fMRI, EEG, and MEG via unified sampling, sparse mixture-of-experts attention, and dual-domain masked modeling, achieving superior cross-modal neuroscience performance.

Hanning Guo, Hanwen Bi, Farah Abdellatif, Andrei Galbenus and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 1/5