Explore tweets tagged as #self_supervised_learning
Excited to share our new paper on DynaCLR, a self-supervised model for learning cell dynamics from terabytes of live imaging!. Enables:.- Detection of infection & cell division.- Label-free prediction of states.- Alignment of async cell trajectories.- Organelle remodeling. (1/4)
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Our perception systems adapt to complexity with two core strategies: self-supervised learning (robots learn from what they see) and foundation models to understand 3D terrain using visual data trained in 2D. Smarter navigation, safer maneuvering, reliable decision-making. No GPS.
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~400 people have joined us on Sunday at @Cohere_Labs Open Science Community ML Summer School. @TimDarcet as always, delivering a super amazing talk on Scaling Self Supervised Learning (SSL, Dinov2, Masked Image Modeling, CAPI).Super interesting session.
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Join our lab's presentations at ICML'2025 @icmlconf in beautiful Vancouver!. 1. Thursday, Olga Ovcharenko (@o_ovcharenko) will present our work with @sscdotopen and @vogt_je on "scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data", selected for a spotlight
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🌟 Article of the Week 🌟.Here’s this week’s must-read contribution to the field from Geoscience and Remote Sensing Society Section within IEEE Access. 🔍 This Week's Highlight:.Title: Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection.Authors: Omid
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Structure-based self-supervised learning enables ultrafast protein stability prediction upon mutation Read more @The_InnovationJ.#research #Science #TIC2025 #LifeSciences
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Incredible session by @TimDarcet on "Scaling Self-Supervised Learning for Vision: An Introduction to DINOv2"! The concepts of self-supervision for computer vision were brilliantly explained. So much to explore there! .#ComputerVision #SelfSupervisedLearning.
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New paper out - accepted at.@ICCVConference . We introduce MoSiC, a self-supervised learning framework that learns temporally consistent representations from video using motion cues. Key idea: leverage long-range point tracks to enforce dense feature coherence across time.🧵
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A recent article published in @PASPJournal describes an automatic and efficient hybrid CNN transformer-based method which incorporates self-supervised learning to identify strong lenses within Dark Energy Spectroscopic Instrument legacy survey data. -JGM
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Yuexuan Kong, Gabriel Meseguer-Brocal, Vincent Lostanlen, Mathieu Lagrange, Romain Hennequin, "Emergent musical properties of a transformer under contrastive self-supervised learning,"
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Self-supervised learning is transforming factory maintenance-enabling machines to spot problems early for smarter, more efficient manufacturing with fewer unexpected breakdowns. The future of industry is here!
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🔬 Excited to share the publication "Applying Self-Supervised Learning to Image Quality Assessment in Chest CT Imaging" 👉 #self_supervised_learning #feature_representation_learning #convolutional_denoising_autoencoder #task_based_approach #chest #CT
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🚀 Self-supervised learning is transforming AI, reducing reliance on labeled data and driving breakthroughs in LLMs, vision, and more. The future is mostly unlabeled. 👁️🗨️🤖.#SelfSupervisedLearning #AI #MachineLearning #DeepLearning #AIResearch. Visit:
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Self-supervised learning is revolutionizing factory maintenance, helping machines detect subtle issues early for smarter, more reliable, and efficient manufacturing operations!
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Day 1 of ML journey!.Started with “Intro to ML” via @ChaiCodeHQ – amazing live cohort for learning & networking!. 🧠ML ⊂ AI.🚗 AI → Reinforcement Learning (self-driving cars!).📊 Supervised vs Unsupervised models. Next: Linear Regression & Gradient Descent! 🔥.#100DaysOfCode.
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#Day3 | ML Summer School by .@Cohere_Labs . Session: Scaling Self-Supervised Learning for Vision: An Introduction to DINOv2.Speaker: @TimDarcet (PhD @ @Meta AI (FAIR) × @Inria )
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