Rectified LpJEPA is presented as a new JEPA architecture targeting a specific…

Brief

Rectified LpJEPA is presented as a new JEPA architecture targeting a specific representation-learning goal: building sparsity in by design without losing useful task information. The post frames the contribution around sparse, non-negative latent representations and points readers to a paper, code release, and blog explainer for the full method.

title: @KuangYilun: How do we build sparsity into JEPA representations by design, while preserving task-relevant informa...
author: @KuangYilun
contenttype: tweet
publication: Twitter/X
published: 2026-02-03T15:38:21+00:00
sourceurl: https://x.com/KuangYilun/status/2018710657068118513

word_count: 41

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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