How do we build sparsity into JEPA representations by design, while preserving task-relevant information?
Introducing Rectified LpJEPA, a JEPA architecture that learns sparse, non-negative, informative representations through principled distributional regularization. 📐
📄 Paper: arxiv.org/abs/2602.01456
💻 Code: github.com/YilunKuang/rectif…
📝 Blog: yilunkuang.github.io/blog/20…
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