A three-stage pipeline that appends an autoencoder reconstruction error (trained on external normal recordings) to the input and trains attention-based multi-view contrastive representations, claiming SOTA on three EEG/ECG benchmarks.
Auto-regressive moving diffusion models for time series forecasting
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Discrepancy-Aware Contrastive Adaptation in Medical Time Series Analysis
A three-stage pipeline that appends an autoencoder reconstruction error (trained on external normal recordings) to the input and trains attention-based multi-view contrastive representations, claiming SOTA on three EEG/ECG benchmarks.