arXiv:2603.03188v3 Announce Type: replace-cross
Abstract: Density-based clustering is a well-established framework that defines clusters through topological or geometric features of the data-generati...
By Nicola Bariletto, Stephen G. Walker
The paper introduces a new approach to learning disentangled representations by directly sculpting the aggregate posterior using the non‑parametric Maximum Mean Discrepancy (MMD) and programmable priors. It demonstrates that traditional Variational Autoencoders and Adversarial Training consistently fail to enforce a factorized latent space, as quantified by the Latent Predictability Score (LPS). The proposed method achieves state‑of‑the‑art unsupervised statistical independence, a 70% reduction in entanglement, optional semi‑supervised semantic alignment, and precise shaping of the posterior without compromising reconstruction quality.
By Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, Aryan Mokhtari, Sujay Sanghavi
arXiv:2603.18792v3 Announce Type: replace
Abstract: Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decom...
By Jakob L{\o}nborg Christensen, Vedrana Andersen Dahl, Morten Rieger Hannemose, Anders Bjorholm Dahl, Christian F. Baumgartner
arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.
By Frieder Wizgall, Georg Tirpitz, Moritz Seiler, Kerstin Ritter, B\'alint Mucs\'anyi
The paper introduces a ground‑truth framework for disentangling uncertainty into epistemic and aleatoric components using sample‑conditional pointwise posterior risk. It evaluates current methods, finding that Spectral‑normalized Neural Gaussian Processes and Variational Latent Gaussian Processes best recover the ground‑truth uncertainty, while most methods align more closely with posterior variance and miss predictor bias. The study also explores the entanglement of estimated uncertainties and the impact of modeling choices, providing practical guidance and releasing 13 semi‑synthetic datasets for further validation.
By Frieder Wizgall, Georg Tirpitz, Moritz Seiler, Kerstin Ritter, B\'alint Mucs\'anyi
arXiv:2511. 22823v2 Announce Type: replace-cross Abstract: Weakly supervised learning has emerged as a practical alternative to fully supervised learning when complete and accurate labels are costly or infeasible to acquire.
By Miao Zhang, Junpeng Li, Changchun Hua, Yana Yang