arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.
By Omar Coser, Antonio Orvieto, Paolo Soda, Loredana Zollo
arXiv:2608. 06993v1 Announce Type: cross Abstract: Large-scale pretrained time-series models achieve strong results through large-scale pretraining and task-agnostic representation learning, but they rely on abundant, diverse data that industrial and scientific domains often lack.
By Gregor Molan (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Grafika Jati (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Francesco Barchi (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Andrea Acquaviva (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Alja\v{z} Osterman (LE-Tehnika d.o.o., \v{S}uceva 27, Kranj, 4000, Slovenia), Martin Molan (Comtrade AI GmbH, Grafenauweg 8, Zug, 6300, Switzerland)
arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.
By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han
arXiv:2607. 23554v1 Announce Type: cross Abstract: In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (EEG) and heart rate variability (HRV) signals.
By Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody