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#selfsupervisedlearning — Public Fediverse posts

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

  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. 🧠 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

  4. 🧠 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

  5. 🧠 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

  6. Công trình đạt giải Best Paper tại NeurIPS bởi Kevin Wang (Princeton) giới thiệu mạng thần kinh 1000 lớp ứng dụng trong học tăng cường tự giám sát. Kỹ thuật mới cải thiện khả năng học biểu diễn sâu mà không cần dữ liệu nhãn, mở đường cho hệ thống AI hiệu quả và tự chủ hơn. #NeurIPS #AI #MachineLearning #HọcTăngCường #TríTuệNhânTạo #SelfSupervisedLearning #DeepLearning

    reddit.com/r/singularity/comme

  7. 🎥🤖 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

  8. 🎥🤖 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

  9. 🎥🤖 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

  10. 🎥🤖 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

  11. 🎥🤖 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

  12. 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

  13. 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