NextStep-1
— AK (@_akhaliq) 15 août 2025
Toward Autoregressive Image Generation with Continuous Tokens at Scale pic.twitter.com/WXY2cukV9d
NextStep-1 Toward Autoregressive Image Generation with Continuous Tokens at Scale
MACHINE LEARNING
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NextStep-1: Autoregressive Image Generation with Continuous Tokens
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Training Agentic Models for Complex Task Performance
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I do agree with that. I feel something needs to fundamentally change in the way we train these models based on different applications. Fundamentally, GPT-5 is an agentic model trained on complex tasks, so of course it will perform best in that latent space when elicited
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Categorical Data Analysis in Statistical Modeling with R
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Categorical data analysis is a fundamental aspect of statistical modeling, often used when the variables in a dataset are qualitative rather than quantitative.
https://
pyoflife.com/analysis-of-ca
tegorical-data-with-r/
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#DataScience #statisticalanalysis #rstats #DataScientists #machinelearning #datavisualizations -
AI’s Future Impact Across Healthcare, Space, Transportation, and Beyond
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Excited for the future in front of us, across all dimensions: healthcare, space, transportation, agriculture, defense, education, entertainment and every other domain of humanity.
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Super-Efficient Tiny AI Model Released
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It's a super-efficient tiny model. You can learn more about it here
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Meta releases DINOv3 computer vision model under commercial license
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To help foster innovation and collaboration in the computer vision community, we’re releasing DINOv3 under a commercial license with a full suite of pre-trained backbones, adapters, training and evaluation code, and (much!) more. Find them here: https://
github.com/facebookresear
ch/dinov3
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DINOv3 Models Now Available in Hugging Face Transformers
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We're excited to share that we have Day-0 support in Hugging Face Transformers for DINOv3 so people can easily leverage the full family of models. Find out more on @huggingface here:
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DINOv3: State-of-the-Art Self-Supervised Vision Model
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Introducing DINOv3: a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing dense… pic.twitter.com/nwS3zFCaaN
— AI at Meta (@AIatMeta) 14 août 2025Introducing DINOv3: a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing dense
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DINOv3: Self-Supervised Learning for Large-Scale Vision Models
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A few highlights of DINOv3 👇
— AI at Meta (@AIatMeta) 14 août 2025
1️⃣SSL enables 1.7B-image, 7B-param training without labels, supporting annotation-scarce scenarios including satellite imagery
2️⃣Produces excellent high-resolution features and state-of-the art performance on dense prediction tasks
3️⃣Diverse… pic.twitter.com/6glMC1S3KWA few highlights of DINOv3 SSL enables 1.7B-image, 7B-param training without labels, supporting annotation-scarce scenarios including satellite imagery
Produces excellent high-resolution features and state-of-the art performance on dense prediction tasks
Diverse