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#selfsupervisedlearning — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #selfsupervisedlearning, aggregated by home.social.

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  1. 🧠 New preprint by Fabian A. Mikulasch & @fzenke: Understanding Self-Supervised #Learning via #LatentDistribution Matching proposes a unifying theoretical framework for #SelfSupervisedLearning.

    The paper reframes #SSL as latent distribution matching, connecting contrastive, non-contrastive, predictive, and stop-gradient methods through a common probabilistic principle linking alignment, uniformity, and latent entropy.

    📝 arxiv.org/abs/2605.03517

    #MachineLearning #RepresentationLearning #AI

  2. 🧠 New preprint by Fabian A. Mikulasch & @fzenke: Understanding Self-Supervised #Learning via #LatentDistribution Matching proposes a unifying theoretical framework for #SelfSupervisedLearning.

    The paper reframes #SSL as latent distribution matching, connecting contrastive, non-contrastive, predictive, and stop-gradient methods through a common probabilistic principle linking alignment, uniformity, and latent entropy.

    📝 arxiv.org/abs/2605.03517

    #MachineLearning #RepresentationLearning #AI

  3. 🎥🤖 Watch as #AI visionary Yann LeCun tries to unlock the secrets of the universe using self-supervised learning, while we pretend to understand anything beyond "AI good." 🚀🌐 Spoiler alert: by 2025, we'll still be watching cat videos. 😂📺
    youtube.com/watch?v=yUmDRxV0krg #YannLeCun #SelfSupervisedLearning #Technology #CatVideos #Future2025 #HackerNews #ngated

  4. 🎥🤖 Watch as #AI visionary Yann LeCun tries to unlock the secrets of the universe using self-supervised learning, while we pretend to understand anything beyond "AI good." 🚀🌐 Spoiler alert: by 2025, we'll still be watching cat videos. 😂📺
    youtube.com/watch?v=yUmDRxV0krg #YannLeCun #SelfSupervisedLearning #Technology #CatVideos #Future2025 #HackerNews #ngated

  5. Read Meta's V-JEPA 2 paper: a self-supervised vision model scaling from 2M to 22M pretraining videos.

    All that effort for just +1% in accuracy. But in ML, every percent counts.

    That’s the price of progress when the low-hanging fruit is gone: we’re now chasing the long tail of rare edge cases. One more percent could be what makes a model truly reliable.

    arxiv.org/html/2506.09985v1

    #ML #AI #SelfSupervisedLearning