60 citations · 175 across the 14 of their papers we have counts for
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cs.LG2023
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Pinar Demetci, Quang Huy Tran, Ievgen Redko +1
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformati…
cs.LG2023★ 1 cited
One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data
Michal Golovanevsky, Eva Schiller, Akira Nair +3
Multimodal learning models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to autonomous driving. Des…