Topic
self-supervised learning
SL-S4Wave: Self-Supervised Learning Framework Improves ECG and EEG Analysis with State Space Models
Researchers propose SL-S4Wave, a self-supervised learning framework combining contrastive learning with structured state space models (S4) to analyze long-sequence physiological waveforms. The model outperforms state-of-the-art baselines in arrhythmia detection and EEG tasks, demonstrates strong label efficiency, and generalizes to unseen arrhythmia types.
Adaptive Binning Boosts Self-Supervised Learning on Medical Tabular Data, Researchers Report
Researchers propose Adaptive Binning, a training-adaptive discretization pretext for tabular self-supervised learning. The method progressively refines discretization per feature and uses a heterogeneity-aware objective. Experiments on public medical tabular datasets show consistent gains over fixed binning approaches.
MoFore: A New Self-Supervised Framework Learns Video Representations by Forecasting Future Latent Embeddings
A new self-supervised video representation learning framework called MoFore (Momentum-Guided Semantic Forecasting) is introduced by researcher Xu Qinwu. Instead of reconstructing masked pixels or aligning contrastive pairs, MoFore learns by forecasting future latent embeddings from temporally distant clips. Experiments on the UCF101 dataset show strong temporal stability and emergent category-level structure without action labels.