A JEPA-based model with domain-informed multi-view self-distillation learns light-curve representations that outperform hand-crafted features on 15 of 16 StarEmbed metrics and adapts competitively to other irregular time-series datasets.
Implementation and Applications on Kepler Data
2 Pith papers cite this work. Polarity classification is still indexing.
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astro-ph.IM 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Astra-CLR is a multi-filter time-series Transformer pre-trained via contrastive learning on 2.1 million ZTF light curves that achieves 0.70 accuracy classifying 12 variability classes, rising to 0.77 with partial fine-tuning.
citing papers explorer
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Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
A JEPA-based model with domain-informed multi-view self-distillation learns light-curve representations that outperform hand-crafted features on 15 of 16 StarEmbed metrics and adapts competitively to other irregular time-series datasets.
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Multi-Scale Contrastive Attention for Light-Curve Representation Learning
Astra-CLR is a multi-filter time-series Transformer pre-trained via contrastive learning on 2.1 million ZTF light curves that achieves 0.70 accuracy classifying 12 variability classes, rising to 0.77 with partial fine-tuning.