FDN uses spectral decomposition, asymmetric heads for deterministic and probabilistic wrench components, and frequency-aware filtering to forecast high-frequency wrench from proprioception, outperforming baselines on hydraulic manipulator grinding data after pretraining and transfer.
Fedformer: Frequency enhanced decomposed transformer for long-term series fore- casting
5 Pith papers cite this work. Polarity classification is still indexing.
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KEMM-Net enriches power load time series representations with cross-modal text and visual knowledge via PID-guided contrastive learning to outperform baselines in few-shot forecasting scenarios.
MedMamba introduces a principle-guided bidirectional multi-scale Mamba model that outperforms prior methods on EEG, ECG, and activity classification benchmarks while delivering 4.6x inference speedup.
PRISM combines data-dependent channel weighting via expert ensemble and confidence-filtered pseudo-label domain adaptation to outperform prior methods on cross-subject EEG emotion tasks in DEAP, DREAMER, and SEED.
A Mamba-plus-attention hybrid with FFT-Laplace and TCN encoding claims state-of-the-art accuracy and efficiency on eight multivariate time-series forecasting benchmarks.
citing papers explorer
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Frequency-aware Decomposition Learning for Sensorless Wrench Forecasting on a Vibration-rich Hydraulic Manipulator
FDN uses spectral decomposition, asymmetric heads for deterministic and probabilistic wrench components, and frequency-aware filtering to forecast high-frequency wrench from proprioception, outperforming baselines on hydraulic manipulator grinding data after pretraining and transfer.
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Beyond Information Redundancy: Expanding Cross-Modal Knowledge Representation for Power Load Time Series Forecasting
KEMM-Net enriches power load time series representations with cross-modal text and visual knowledge via PID-guided contrastive learning to outperform baselines in few-shot forecasting scenarios.
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MedMamba: Recasting Mamba for Medical Time Series Classification
MedMamba introduces a principle-guided bidirectional multi-scale Mamba model that outperforms prior methods on EEG, ECG, and activity classification benchmarks while delivering 4.6x inference speedup.
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PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition
PRISM combines data-dependent channel weighting via expert ensemble and confidence-filtered pseudo-label domain adaptation to outperform prior methods on cross-subject EEG emotion tasks in DEAP, DREAMER, and SEED.
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UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
A Mamba-plus-attention hybrid with FFT-Laplace and TCN encoding claims state-of-the-art accuracy and efficiency on eight multivariate time-series forecasting benchmarks.