arXiv:2602. 02890v2 Announce Type: replace Abstract: Model soups are strange and strangely effective combinations of parameters.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 23273v1 Announce Type: cross Abstract: Computational nutrition needs precise ingredient data, but current databases are incomplete, inconsistent, and built for human reference rather than automated reasoning.
The paper introduces C-Score, a diagnostic framework for evaluating pseudo‑label‑based semi‑supervised learning (SSL) when unlabeled data may contain out‑of‑distribution (OOD) samples. C-Score assesses training behavior across prediction, feature representation, and optimization, using metrics such as PLE, CCI, Sem‑Drift, and Grad‑Align. Experiments on CIFAR‑10 and CIFAR‑100 with various OOD sources show that C‑Score detects hidden degradation that clean accuracy alone fails to reveal, highlighting the need for internal diagnostic signals in SSL robustness assessment.
arXiv:2407. 05370v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data.
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...
arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.
arXiv:2607. 00113v1 Announce Type: new Abstract: Background.