1 citations · 1 across the 5 of their papers we have counts for
5 papers
Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging
Marco Morik, Xiao Ruiting, Shinichi Nakajima +2
Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterative update rules. Although t…
Attentive multilayer fusion for vision transformers
Laure Ciernik, Marco Morik, Lukas Thede +4
With the rise of large-scale foundation models, efficiently adapting them to downstream tasks remains a central challenge. Linear probing, which freezes the backbone and trains a l…
Federated Learning over Connected Modes
Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local dist…
Labeling Neural Representations with Inverse Recognition
Kirill Bykov, Laura Kopf, Shinichi Nakajima +2
Deep Neural Networks (DNNs) demonstrate remarkable capabilities in learning complex hierarchical data representations, but the nature of these representations remains largely unkno…
Generative Fractional Diffusion Models
Gabriel Nobis, Maximilian Springenberg, Marco Aversa +11
We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Although diffusion models have excell…