3 papers
cs.LG2025
Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation
Maedeh Zarvandi, Michael Timothy, Theresa Wasserer +1
Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific explanat…
cs.LG2024
Explainable Clustering of Mixture Models
Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar
The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with leaves can approximate a given clust…
cs.CV2021
Stateless actor-critic for instance segmentation with high-level priors
Paul Hilt, Maedeh Zarvandi, Edgar Kaziakhmedov +4
Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient trai…