The paper introduces H$^2$EDL, a Hyper Evidential Deep Learning framework that unifies flat evidential classification and hierarchical classification by leveraging the taxonomy itself as a hyperdomain. By assigning one local Dirichlet opinion per branching node, the model generates a linear‑size focal family that captures both fine‑grained class uncertainty and intermediate concept belief. Experiments on FGVC‑Aircraft and DERM12345 show that H$^2$EDL halves calibration error relative to cross‑entropy baselines, especially at deeper hierarchy levels and with larger training budgets.
arXiv:2511. 14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote.
arXiv:2602. 01477v2 Announce Type: replace-cross Abstract: Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks.
arXiv:2609.24276v1 Announce Type: new Abstract: Hyperbolic vision-language models (VLMs) represent image and text features in a geometry naturally suited to hierarchy, but their adaptation to downstr...
arXiv:2509. 05130v2 Announce Type: replace Abstract: In classification problems, models are trained to predict a class label based on the input data features.
The study adapts a convolutional neural network to classify galaxy morphologies using crowd-sourced annotations from Galaxy Zoo 1. It evaluates how training strategies—such as training all layers versus only the last, incorporating hierarchical labels, varying data volume and annotator agreement, staged transfer learning, and ensembling—affect accuracy and efficiency. Results show that full-network training and high annotator agreement yield over 99% accuracy, while hierarchical approaches and staged learning help when data are limited.
The paper proposes treating the ‘unit’—a persistent referent that multiple events may refer to—as an explicit primitive in machine learning tasks. It formalizes supervised learning as learning a pair of a tokenizer that generates a contextual unit token and a shared response law that uses this token, thereby distinguishing homogeneous from heterogeneous worlds. The work also introduces concepts such as unit abduction and trusted resolvers to handle cases where unit identity is unresolved.
The paper introduces Trusted Multi-view learning with Unified Routing (TMUR), a method that separates view-specific evidence extraction from fusion arbitration in multi-view classification. TMUR employs view-private experts, a collaborative expert, and a unified router that assigns sample-level weights based on global context, along with soft load-balancing and diversity regularization to promote balanced and discriminative expert use. Experiments on 14 datasets show that TMUR consistently improves classification accuracy and reliability compared to 15 recent baselines.
arXiv:2606. 07537v1 Announce Type: cross Abstract: Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scales.
arXiv:2605. 13352v2 Announce Type: replace Abstract: Standard dual-encoder vision-language models that map images and text to deterministic points on a shared unit hypersphere through $\ell_2$ normalization typically expose neither \emph{aleatoric} uncertainty (cross-modal ambiguity) nor \emph{epistemic} uncertainty (lack of training-distribution support).
arXiv:2609.37594v1 Announce Type: new Abstract: Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot t...
arXiv:2608.23960v1 Announce Type: cross Abstract: Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of...
arXiv:2609.14451v1 Announce Type: cross Abstract: Modern semi-supervised learning (SSL) couples pseudo-label generation and classifier training, using the classifier's own confidence to select the ps...