Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
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SolarTformer applies self-attention transformers to solar power forecasting and claims to outperform prior models on clear and cloudy days by incorporating power-station metadata for better generalization.
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Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
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SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
SolarTformer applies self-attention transformers to solar power forecasting and claims to outperform prior models on clear and cloudy days by incorporating power-station metadata for better generalization.