🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

We are releasing the 1st version of 4M, a framework for training multimodal foundation models across tens of modalities & tasks, based on scalable masked modeling. Joint effort by EPFL & Apple. 4M: Massively Multimodal Masked Modeling 🌐4m.epfl.ch 🧵1/n

We'll present at NeurIPS, today at 5pm CST. Spotlight #1022. Effectively bringing sensory modalities to large models is one way to make them more grounded, and ultimately have a more complete World Model. This is a step in that direction hopefully, and more will come.

🚀 Model and data for our CubifyAnything project are now released! 🔗 github.com/apple/ml-cubif… #SpatialReasoning #3DObjectDetection #transformers #detection #ai #genai

Excited to share that we have recently released the source code for FlexTok, bringing a fresh perspective to tokenization. Code on GitHub: lnkd.in/g4iNJFmU. Project Page: flextok.epfl.ch #FlexTok #Tokenization #MachineLearning #MLResearch #OpenSource #AI

Singapore can get you off a plane, through immigration, and into a cab in under 30 minutes. But at #ICLR25, you’ll need over 2 hours and a 0.5 mile hike just to get your badge. Congrats to #ICLR for breaking the record for most academic patience ever tested. #ICLR25 #ConfLife

Very excited to announce our final line-up of fantastic speakers at this year's #CVPR2025 workshop on Open-World 3D Scene Understanding with Foundation Models ✨ #OpenSUN3D #cvpr2025 📆 June 12, 2pm-6pm 🏡 opensun3d.github.io

Incredibly proud of the work across teams in delivering the latest version of Visual Intelligence. Visual Intelligence makes it faster to do more with what’s right in front of you. #WWDC25 #visualintelligence #AppleIntelligence

Excited to share our new work: “Language Models Improve When Pretraining Data Matches Target Tasks” Yes, it sounds obvious (and it is!), but typically this only happens implicitly and indirectly: intuitively select data → benchmark → refine → repeat. We wondered: what

Yesterday we shared our latest work on pretraining data curation. What if we stop guessing which data is “good” and directly match pretraining data to the benchmarks we care about? 📄 arxiv.org/abs/2507.12466 #AIResearch #llm #DataCuration #Pretraining #ScalingLaws