AutoPV applies neural architecture search with a custom search space drawn from time series forecasting and photovoltaic models to automatically produce architectures that outperform predefined state-of-the-art models on a Chinese solar station dataset.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
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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.
citing papers explorer
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AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
AutoPV applies neural architecture search with a custom search space drawn from time series forecasting and photovoltaic models to automatically produce architectures that outperform predefined state-of-the-art models on a Chinese solar station dataset.
- UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration