#semi-supervised learning

6 results
cond-mat.dis-nn2026

Semi-supervised Hopfield model: Theoretical and Numerical results

Linda Albanese, Andrea Ladiana, Andrea Lepre

The paper develops a statistical‑mechanical theory for semi‑supervised learning in Hopfield networks by mixing supervised and unsupervised Hebbian couplings, deriving performance t…

#semi-supervised learning#hopfield network#hebbian learning#replica theory
cs.LG2026

Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus

Rasmus Tirsgaard, Laurits Fredsgaard, Marisa Wodrich +2

The paper proposes a semi-supervised learning approach for molecular graph data that uses an ensemble consensus objective to improve prediction accuracy, robustness, and calibratio…

#semi-supervised learning#molecular graphs#graph neural networks#ensemble methods
cs.LG2026

Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite

Yushi Hirose, Hiroo Irobe, Takafumi Kanamori

The paper introduces a generalized, distribution‑free framework for semi‑supervised learning that builds unbiased risk estimators for both binary and multiclass problems, achieving…

#semi-supervised learning#distribution-free learning#multiclass classification#risk estimation
cs.LG2026

Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization

Adam M. Oberman

The paper provides a theoretical analysis showing that self‑supervised learning with data augmentation can achieve a fast O(1/n_L) error rate in semi‑supervised settings, linking t…

#semi-supervised learning#self-supervised learning#data augmentation#graph regularization
cs.CV2026

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?

Nusrat Munia, Tyler Ward, Nishat Nayla +2

The paper empirically compares pretraining‑finetuning and joint training of self‑supervised and supervised objectives across multiple SSL methods and vision tasks, showing that joi…

#self-supervised learning#joint training#pretraining#semi-supervised learning
cs.CV2026

Temporal Feature Distillation for Label-Efficient Precise Event Spotting in Sports Videos

Hao Xu, Xinyu Wei, Sam Wells +1

The paper introduces a semi-supervised Temporal Feature Distillation method and a Transformer Gate Shift module to improve precise event spotting in sports videos with limited labe…

#event spotting#sports video analysis#semi-supervised learning#temporal modeling