What is Hackers' Pub?

Hackers' Pub is a place for software engineers to share their knowledge and experience with each other. It's also an ActivityPub-enabled social network, so you can follow your favorite hackers in the fediverse and get their latest posts in your feed.

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:petthex_javasparrow:しゅいろ:petthex_javasparrow:(本物) @syuilo@misskey.io
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Misskeyは個人開発です​:blob_bongo_cat_keyboard:​
今後も開発を続けられるよう、よろしければMisskey Projectへのご寄付をお願いします🙏🙏🙏
支援特典もございます​:ai_blink_nod:​
https://misskey-hub.net/ja/docs/donate/

プロジェクトへの寄付 | Misskey

分散型SNS作成ソフトウェア Misskeyの公式サイト。Misskeyサーバーの作り方やサーバー一覧など、Misskeyに関する情報が満載！

misskey-hub.net · Misskey

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nulta @nulta@mi.rerac.dev
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Shared by nulta


송금하기 전 먼저 듀얼으로
본인확인을 해야 해요

✓ [필수] 개인정보 제3자 제공동의
✓ [선택] 어둠의 듀얼 마케팅 수신동의

　​:blank:​은행원과 듀얼하기​:blank:​　
러시 듀얼으로 대체하기

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보이저 공지 @N@town.voyager.blue
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Shared by 리피 :ydg: :verified:

여행자 여러분, 안녕하세요.

현재 발생 중인 Cloudflare 문제로 인한 서버 접속 지장 완화를 위해 Cloudflare 프록싱을 해제하였습니다. 기본적인 기능은 사용하실 수 있으나, 미디어 관련 기능(프로필, 헤더, 게시물에 포함된 사진 등의 로딩이나 미디어 업로드)능 동작하지 않습니다. 사용에 참고 부탁드립니다.

최대한 할 수 있는 조치를 취하겠습니다. 이용에 불편을 드려서 죄송합니다.

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Hacker News 50 @hn50@social.lansky.name
Public

LeJEPA

Link: arxiv.org/abs/2511.08544
Discussion: news.ycombinator.com/item?id=4

arXiv logo

LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Learning manipulable representations of the world and its dynamics is central to AI. Joint-Embedding Predictive Architectures (JEPAs) offer a promising blueprint, but lack of practical guidance and theory has led to ad-hoc R&D. We present a comprehensive theory of JEPAs and instantiate it in {\bf LeJEPA}, a lean, scalable, and theoretically grounded training objective. First, we identify the isotropic Gaussian as the optimal distribution that JEPAs' embeddings should follow to minimize downstream prediction risk. Second, we introduce a novel objective--{\bf Sketched Isotropic Gaussian Regularization} (SIGReg)--to constrain embeddings to reach that ideal distribution. Combining the JEPA predictive loss with SIGReg yields LeJEPA with numerous theoretical and practical benefits: (i) single trade-off hyperparameter, (ii) linear time and memory complexity, (iii) stability across hyper-parameters, architectures (ResNets, ViTs, ConvNets) and domains, (iv) heuristics-free, e.g., no stop-gradient, no teacher-student, no hyper-parameter schedulers, and (v) distributed training-friendly implementation requiring only $\approx$50 lines of code. Our empirical validation covers 10+ datasets, 60+ architectures, all with varying scales and domains. As an example, using imagenet-1k for pretraining and linear evaluation with frozen backbone, LeJEPA reaches 79\% with a ViT-H/14. We hope that the simplicity and theory-friendly ecosystem offered by LeJEPA will reestablish self-supervised pre-training as a core pillar of AI research (\href{https://github.com/rbalestr-lab/lejepa}{GitHub repo}).

arxiv.org · arXiv.org

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