VCR learns valid contextual representations for incomplete wearable signals via orthogonal disentanglement and missing-aware mixture-of-experts, improving robustness across full and missing-modality settings.
Lsm-2: Learning from incomplete wearable sensor data
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 4years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
xMAE pretrains biosignal representations via masked cross-modal reconstruction of temporally ordered signals like ECG and PPG, outperforming baselines on 15 of 19 downstream tasks including cardiovascular prediction and sleep staging.
WavesFM uses hierarchical SSL to pretrain a segment encoder on short waveforms followed by a temporal encoder on multi-day sequences, outperforming prior methods on 58 tasks after training on over 12 million hours of data from hundreds of thousands of people.
SentryFuse delivers modality-aware zero-shot pruning and sparse attention that improves accuracy by 12.7% on average and up to 18% under sensor dropout while cutting memory 28.2% and latency up to 1.63x across multimodal edge models.
citing papers explorer
-
VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals
VCR learns valid contextual representations for incomplete wearable signals via orthogonal disentanglement and missing-aware mixture-of-experts, improving robustness across full and missing-modality settings.
-
Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning
xMAE pretrains biosignal representations via masked cross-modal reconstruction of temporally ordered signals like ECG and PPG, outperforming baselines on 15 of 19 downstream tasks including cardiovascular prediction and sleep staging.
-
WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms
WavesFM uses hierarchical SSL to pretrain a segment encoder on short waveforms followed by a temporal encoder on multi-day sequences, outperforming prior methods on 58 tasks after training on over 12 million hours of data from hundreds of thousands of people.
-
Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference
SentryFuse delivers modality-aware zero-shot pruning and sparse attention that improves accuracy by 12.7% on average and up to 18% under sensor dropout while cutting memory 28.2% and latency up to 1.63x across multimodal edge models.