3 papers
q-bio.NC2026
Attraction to hierarchical feature memory explains orientation bias
Kira Michaela Düsterwald, Peter Vincent, Ana Kapros +2
When recalling the orientation of recent stimuli, observers are systematically biased away from the cardinal axes. The prevailing explanation is that this ''anti-cardinal bias'' ar…
cs.LG2024
Fast unsupervised ground metric learning with tree-Wasserstein distance
Kira M. Düsterwald, Samo Hromadka, Makoto Yamada
The performance of unsupervised methods such as clustering depends on the choice of distance metric between features, or ground metric. Commonly, ground metrics are decided with he…
stat.ML2023
An Empirical Study of Self-supervised Learning with Wasserstein Distance
Makoto Yamada, Yuki Takezawa, Guillaume Houry +4
In this study, we delve into the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where…