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Sjoerd van Steenkiste

@svansteenkiste.bsky.social
101 followers 56 following 24 posts

Research Scientist @GoogleDeepMind. World models / Reasoning / Tool-use / Gemini.

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Reposted by Sjoerd van Steenkiste
Andrew Lampinen @lampinen.bsky.social · 02/05/2025
How do language models generalize from information they learn in-context vs. via finetuning? In arxiv.org/abs/2505.00661 we show that in-context learning can generalize more flexibly, illustrating key differences in the inductive biases of these modes of learning — and ways to improve finetuning. 1/
arxiv.org
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Reposted by Sjoerd van Steenkiste
MAPS - CVPR 2026 Workshop @mapscvpr.bsky.social · 17/04/2025
🚨 Deadline Extension Alert for #VLMs4All Challenges! 🚨 We have extended the challenge submission deadline 🛠️ New challenge deadline: Apr 22 Show your stuff in the CulturalVQA and GlobalRG challenges! 👉 sites.google.com/view/vlms4al... Spread the word and keep those submissions coming! 🌍✨
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 14/03/2025
Excited to announce that we will be organizing a #CVPR2025 Workshop on Building Geo-Diverse and Culturally Aware VLMs. Aside from fantastic speakers and a short-paper track, the workshop includes two challenges, one of them based on our CulturalVQA benchmark. Links below!
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 27/02/2025
arxiv.org/abs/2311.00445
arxiv.org
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 27/02/2025
arxiv.org/abs/2310.19956
arxiv.org
The Impact of Depth on Compositional Generalization in Transformer Language Models
To process novel sentences, language models (LMs) must generalize compositionally -- combine familiar elements in new ways. What aspects of a model's structure promote compositional generalization? Fo...
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 27/02/2025
arxiv.org/abs/2409.04556
arxiv.org
How Does Code Pretraining Affect Language Model Task Performance?
Large language models are increasingly trained on corpora containing both natural language and non-linguistic data like source code. Aside from aiding programming-related tasks, anecdotal evidence sug...
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 27/02/2025
openreview.net/forum?id=arY...
openreview.net
Can Language Models Perform Implicit Bayesian Inference Over User...
To successfully interact with the world, both humans and machines need to construct models of the world and form beliefs about these models. These beliefs need to be updated as new information...
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 27/02/2025
Application page: www.google.com/about/career... Some recent papers from our team below:
google.com
Research Intern, PhD, Summer 2025 — Google Careers
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 27/02/2025
Our team @GoogleAI is hiring an intern. We are interested in having LMs understand and respond to users better. Topics include: teaching LMs to build “mental models” of users; improving LM's reasoning capability over long contexts. @GoogleAI internship deadline is Feb 28.
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Reposted by Sjoerd van Steenkiste
Ibrahim Alabdulmohsin @ibomohsin.bsky.social · 12/02/2025
🔥Excited to introduce RINS - a technique that boosts model performance by recursively applying early layers during inference without increasing model size or training compute flops! Not only does it significantly improve LMs, but also multimodal systems like SigLIP. (1/N)
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Reposted by Sjoerd van Steenkiste
Ibrahim Alabdulmohsin @ibomohsin.bsky.social · 25/01/2025
If you are interested in developing large-scale, multimodal datasets & benchmarks, and advancing AI through data-centric research, check out this great opportunity. Our team is hiring! boards.greenhouse.io/deepmind/job...
boards.greenhouse.io
Research Scientist, Zurich
Zurich, Switzerland
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 22/01/2025
The ICLR 2025 decisions are out! It was an honor to serve as a Senior Area Chair for this year’s iteration, and be more involved in overseeing the review process.
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Reposted by Sjoerd van Steenkiste
ICLR Conference @iclr-conf.bsky.social · 21/01/2025
Financial Assistance applications are now open! If you face financial barriers to attending ICLR 2025, we encourage you to apply. The program offers prepay and reimbursement options. Applications are due March 2nd with decisions announced March 9th. iclr.cc/Conferences/...
iclr.cc
ICLR 2024 Financial Assistance
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Reposted by Sjoerd van Steenkiste
Mehdi S. M. Sajjadi @msajjadi.com · 13/01/2025
Check out @tkipf.bsky.social's post on MooG, the latest in our line of research on self-supervised neural scene representations learned from raw pixels: SRT: srt-paper.github.io OSRT: osrt-paper.github.io RUST: rust-paper.github.io DyST: dyst-paper.github.io MooG: moog-paper.github.io
srt-paper.github.io
Scene Representation Transformer
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Mehdi S. M. Sajjadi @msajjadi.com · 10/01/2025
TRecViT: A Recurrent Video Transformer arxiv.org/abs/2412.14294 Causal, 3× fewer parameters, 12× less memory, 5× higher FLOPs than (non-causal) ViViT, matching / outperforming on Kinetics & SSv2 action recognition. Code and checkpoints out soon.
TRecViT architecture
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 15/12/2024
with @linluqiu.bsky.social Fei Sha, Kelsey Allen, Yoon Kim, @tallinzen.bsky.social and myself.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 15/12/2024
Can language models perform implicit Bayesian inference over user preference states? Come find out at the “System-2 Reasoning at Scale” #NeurIPS2024 workshop, 11:30pm West Ballroom B.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 12/12/2024
Neural Assets poster is happening now. Join us at East Exhibit Hall A-C #1507
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 11/12/2024
I will be at the @GoogleAI booth until 2pm. Come say hello if you have questions about Google Research!
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 11/12/2024
Excited to be at #NeurIPS2024. A few papers we are presenting this week: MooG: arxiv.org/abs/2411.05927 Neural Assets: arxiv.org/abs/2406.09292 Probabilistic reasoning in LMs: openreview.net/forum?id=arYXg… Let’s connect if any of these research topics interest you!
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 10/12/2024
Interesting perspective on ICL and great suggestions for future research in this space!
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Reposted by Sjoerd van Steenkiste
Andreas Steiner @andreaspsteiner.bsky.social · 05/12/2024
🚀🚀PaliGemma 2 is our updated and improved PaliGemma release using the Gemma 2 models and providing new pre-trained checkpoints for the full cross product of {224px,448px,896px} resolutions and {3B,10B,28B} model sizes. 1/7
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Andrii Zadaianchuk 🇺🇦 @zadaianchuk.bsky.social · 29/11/2024
Looking forward to seeing what is possible to build on top of such "particle" representations. While conceptually simple, they are one step closer to represent scenes (underlying causal structure) not videos (mixture of the many factors together), and could be useful for robotics tasks.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 29/11/2024
That looks amazing, enjoy!
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 25/11/2024
If you are reviewing for ICLR, please engage with the author response!
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
This project is the result of a wonderful collaboration with many people at Google, and will appear at NeurIPS later this year. Special thanks to my co-first authors @zdanielz.bsky.social and @tkipf.bsky.social for being great collaborators and seeing this project through!
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
While the vast majority of computer vision advances in the past decade can be attributed to successful “on-the-grid” architectures such as CNNs and Vision Transformers, the physical world ultimately does not live on a pixel grid, which we address in MooG.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
Even in comparison to specialized architectures for down-stream tasks, such as TAPIR for point-tracking, we find that self-supervised MooG latents yield strong performance.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
MooG can provide a strong foundation for different downstream vision tasks, including point tracking, monocular depth estimation, and object tracking. Especially when reading out from frozen representations, MooG tends to outperform on-the-grid baselines.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
We demonstrate the usefulness of MooG’s learned representation both qualitatively and quantitatively by training readouts on top of the learned representation on a variety of downstream tasks.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
Inspired by prior methods using slots or queries, MooG uses cross-attention to disentangle the representation structure and image structure. Combined with a next frame prediction loss, this results in latent tokens that bind to specific scene structures and track them as they move.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
MooG is a self-supervised video representation model that combines transformers and recurrence to update latent tokens in a stage-wise manner.
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Sjoerd van Steenkiste @svansteenkiste.bsky.social · 20/11/2024
Excited to announce MooG for learning video representations. MooG allows tokens to move “off-the-grid” enabling better representation of scene elements, even as they move across the image plane through time. 📜https://arxiv.org/abs/2411.05927 🌐https://moog-paper.github.io/
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