Topic
state space models
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.
RoboSSM Introduces State-Space Models for Scalable In-Context Imitation Learning in Robotics
RoboSSM is a new method for in-context imitation learning (ICIL) that replaces Transformer-based architectures with state-space models (SSMs). The approach uses Longhorn, a state-of-the-art SSM, enabling linear-time inference and strong extrapolation to longer prompts. Experiments on the LIBERO benchmark show improved generalization to unseen and long-horizon tasks compared to Transformer-based ICIL methods.