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
4 Pith papers cite this work. Polarity classification is still indexing.
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UniMamba integrates Mamba state-space dynamics with attention layers and transforms like FFT-Laplace to outperform prior models on multivariate time series forecasting benchmarks.
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.
PrismNet combines text and image modalities with time series via a PID-guided contrastive learning module to boost few-shot power load forecasting accuracy and provide interpretability.
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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UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
UniMamba integrates Mamba state-space dynamics with attention layers and transforms like FFT-Laplace to outperform prior models on multivariate time series forecasting benchmarks.
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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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PrismNet: Viewing Time Series Through a Multi-Modal Prism for Interpretable Power Load Forecasting
PrismNet combines text and image modalities with time series via a PID-guided contrastive learning module to boost few-shot power load forecasting accuracy and provide interpretability.