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Posts with #SelfSupervisedLearning

#SelfSupervisedLearning

Public posts on X that use #SelfSupervisedLearning.

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  5. Singing in the Cloud@SingingInDCloud
    Traditional machine learning depends heavily on manually labeled datasets—time-consuming, expensive, and often limited in scale. But Self-Supervised Learning (SSL) is changing the game. Delve deeper on DeltalogiX > #ML #SelfSupervisedLearning #AI.
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    223196
  6. Linda Grasso✓@LindaGrass0
    Traditional machine learning depends heavily on manually labeled datasets—time-consuming, expensive, and often limited in scale. But Self-Supervised Learning (SSL) is changing the game. Delve deeper on DeltalogiX > #ML #SelfSupervisedLearning #AI
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    245473.6K
  9. Supervised Feed@FeedSupervised
    Traditional machine learning depends heavily on manually labeled datasets—time-consuming, expensive, and often limited in scale. But Self-Supervised Learning (SSL) is changing the game. Delve deeper on DeltalogiX > #ML #SelfSupervisedLearning #AI.
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    025369
  10. Mengmi Zhang@MengmiZhang
    🚀 Our paper at ICLR26 show that powerful priors can be learned entirely from object instances (no labels, no language, no manual annotations) -> test-time iterative inference and true OOD generalization. Read here: #ICLR2026 #SelfSupervisedLearning #AI
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    19756.5K
  11. Pearse Keane@pearsekeane
    Great article by @EricTopol - exciting to see how the vision he has laid out over past decade is really starting to take shape! #SelfSupervisedLearning #Transformers As artificial intelligence goes multimodal, medical applications multiply | Science
    0197727.3K
  12. Lightning AI ⚡️✓@LightningAI
    🎉 Turns out Autoencoding can work almost* as well as contrastive learning for #SelfSupervisedLearning! Check out the newest Grid AI Labs paper by @williamfalcon @AnanyaHarsh @teddykoker @kchonyc 👇🏾
    Grid.ai @gridai_
    🎉Excited to share the latest paper from Grid AI Labs: A new approach to #SelfSupervisedLearning based on autoencoding (AAVAE), which achieves comparable results to contrastive methods while reducing training complexity. Paper Code
    0531
  13. Grid.ai@gridai_
    🎉Excited to share the latest paper from Grid AI Labs: A new approach to #SelfSupervisedLearning based on autoencoding (AAVAE), which achieves comparable results to contrastive methods while reducing training complexity. Paper Code
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    31262
  21. CyrusAI@CyrusAiCo
    🚀 Dino v3’s 7B ViT model sets new SOTA by training on 1.7B images using self-supervised learning—pushing vision tasks to new heights! A glimpse into the future of scalable, efficient AI models. #AI #SelfSupervisedLearning #ComputerVision via Reddit via Reddit
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