New Apple #ML Research Highlight: The "Super Weight:" How Even a Single Parameter can Determine an #LLM's Behavior machinelearning.apple.com/research/the...

scaling up the computation of optimal transport couplings to hundreds of thousands of 3k dimensional vectors made easy using sharding and OTT-JAX! check this notebook, it only takes a few lines of code thanks to JAX's native sharding abilities ott-jax.readthedocs.io/en/latest/tu...

New Apple #ML Research Highlight: "FastVLM: Efficient Vision Encoding for Vision Language Models" machinelearning.apple.com/research/fas...

So pleased and proud to share with you what our team has been up to, on an ambitious journey to build a video foundation model for scientific domains ! ✨ 🚀 🎞️ 🧪 #ICCV2025 #AI4Science

The NeurIPS paper checklist corroborates the bureaucratic theory of statistics.

Can LLMs access and describe their own internal distributions? With my colleagues at Apple, I invite you to take a leap forward and make LLM uncertainty quantification what it can be. 📄 arxiv.org/abs/2505.20295 💻 github.com/apple/ml-sel... 🧵1/9

Shunichi Amari has been awarded the 40th (2025) Kyoto Prize in recognition of his pioneering research in the fields of artificial neural networks, machine learning, and information geometry www.riken.jp/pr/news/2025...

NEW PAPER ALERT: Recent studies have shown that LLMs often lack robustness to distribution shifts in their reasoning. Our paper proposes a new method, AbstRaL, to augment LLMs’ reasoning robustness, by promoting their abstract thinking with granular reinforcement learning.

Now that @interspeech.bsky.social registration is open, time for some shameless promo! Sign-up and join our Interspeech tutorial: Speech Technology Meets Early Language Acquisition: How Interdisciplinary Efforts Benefit Both Fields. 🗣️👶 www.interspeech2025.org/tutorials ⬇️ (1/2)

Today we have released the code and a demo iOS application for FastVLM - our extremely efficient and fast vision language model which runs on your device using MLX! You can check out the code and the app here: github.com/apple/ml-fas...

#ICLR #TrainBetterLM I am at ICLR, come to our posters for improved language model training! Recycle gradients for faster neural net training with AdEMAmix iclr.cc/virtual/2025... (Fri Apr 25, 10 am). 1/3

New post: "Apple Machine Learning Research at #ICLR 2025" - highlighting a selection of the many Apple #ML research papers to be presented at @iclr-conf.bsky.social this week: machinelearning.apple.com/research/icl...

Thrilled to share the latest work from our team at @Apple where we achieve interpretable and fine-grained control of LLMs and Diffusion models via Activation Transport 🔥 📄 arxiv.org/abs/2410.23054 🛠️ github.com/apple/ml-act 0/9 🧵

The trader made millions. Why was this unusual? For a few reasons: - Firstly new opening volume on chain, minutes after market open - IVR of +80 on $QQQ, with iv percentile of medium. - happened at once, at ask -otm - market was bearish, across the board Unusual. Come learn: unusualwhales.com

I'm calling for an investigation into whether President Trump manipulated the market to benefit his Wall Street donors—all while working people and small businesses paid the price. Did Trump help insiders cash in on his tariff flip-flopping? It sure looks like corruption.

Accepted as a Spotlight at @iclr-conf.bsky.social the work shares a new method for fine-grained control over #genAI output - without the computational overhead, complexity, and volume of data needed by #RLHF or fine-tuning, and with more reliable results than prompt engineering.

Paper🧵 (cross-posted at X): When does composition of diffusion models "work"? Intuitively, the reason dog+hat works and dog+horse doesn’t has something to do with independence between the concepts being composed. The tricky part is to formalize exactly what this means. 1/

because "attention is all you need"

🚨 One question that has always intrigued me is the role of different ways to increase a model's capacity: parameters, parallelizable compute, or sequential compute? We explored this through the lens of MoEs:

Excited to see Sigmoid Attention accepted at ICLR 2025 !! Make attention ~18% faster with a drop-in replacement 🚀 Code: github.com/apple/ml-sig... Paper arxiv.org/abs/2409.04431

Here's a really cool cross-institution study leveraging optimal transport techniques developed by my Apple ML Research colleagues! It's great to see basic research in machine learning translate into scientific tools like this. Cuts into the AI hype a bit ;)

Missing the deep learning part? go check out the follow up work @neuripsconf.bsky.social (tinyurl.com/yvf72kzf) and @iclr-conf.bsky.social (tinyurl.com/4vh8vuzk)

Exciting new tool to make the most out of multimodal single-cell data from our friends and long-term collaborators of the @fabian_theis lab - check it out, it works beautifully and really makes sense out of your complex data! 👇👍😉

Excited to see Moscot (moscot-tools.org) published in @Nature! We scaled Optimal Transport (OT) in single-cell genomics & added multimodality together with spatiotemporal trajectory inference, finding exciting new biology in the pancreas! 🚀 Read at www.nature.com/articles/s41...

@AimeeBastidas, @pacotael, @MartaTarquis, @ShreyParikh07, Ilan Gold, @heikolickert.bsky.social , @mostafabakhti.bsky.social @marcocuturi.bsky.social , @fabiantheis.bsky.social

Nature research paper: Mapping cells through time and space with moscot https://go.nature.com/40pqpvw

Today is a great day for optimal transport 🎉! Lots of gratitude 🙏 for all folks who contributed to ott-jax.readthedocs.io and pushed for the MOSCOT (now @ nature!) paper, from visionaries @dominik1klein.bsky.social, G. Palla, Z. Piran to the magician, Michal Klein! ❤️ www.nature.com/articles/s41...

For those saying it wasn't a Nazi salute. Keep posting it. Keep sharing it.

AI in Cell Research: Unveiling Organ Development #Moscot, a groundbreaking #AI tool developed by an international team led by #HelmholtzMunich, tracks cells in real time, revealing how organs like the #pancreas form. t1p.de/viil5 @fabiantheis.bsky.social @nature.com

The Apple Machine Learning Research (MLR) team in Paris has openings for both FTE roles and a short-term post-doc position to contribute to our team's research agenda. Researchers at Apple's MLR (led by Samy Bengio) target impactful publications in top-tier ML venues and OSS.

The UniReps Workshop is happening THIS SATURDAY at #NeurIPS 🤖🧠 Join us for a day of insightful talks and discussions with @sueyeonchung.bsky.social, @eringrant.bsky.social, @leavittron.bsky.social, @itsneuronal.bsky.social, @marcocuturi.bsky.social, Philip Isola, Neel Nanda and Stefanie Jegelka! 🎤

Check this really nice thread by @paurodriguez.bsky.social !!

I will be at hashtag#neurips2024 for the entire week from Tuesday! Please feel free to reach out if interested in Apple MLR, either in the Apple booth, one of the posters I am involved in (neurips.cc/virtual/2024..., neurips.cc/virtual/2024... neurips.cc/virtual/2024...),

I'm excited to share our NeurIPS 2024 paper "Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms" 🤖. Paper link 📜: arxiv.org/abs/2410.19055

this is starting to feel like transfer to mastodon a couple of years ago except this time it might work? 😊

𝗗𝗼𝗲𝘀 𝗮𝘂𝘁𝗼𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗽𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝘃𝗶𝘀𝗶𝗼𝗻? 🤔 Delighted to share AIMv2, a family of strong, scalable, and open vision encoders that excel at multimodal understanding, recognition, and grounding 🧵 paper: arxiv.org/abs/2411.14402 code: github.com/apple/ml-aim HF: huggingface.co/collections/...