Jannis Born

#transformers #neurips #eurips #ibmresearch | Jannis Born 𝗡𝗲𝘂𝗿𝗜𝗣𝗦 𝘀𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁 for our work on "𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗗𝗼𝘂𝗯𝗹𝘆 𝗦𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀" 🔦 𝘊𝘢𝘯 𝘱𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦𝘴 𝘧𝘳𝘰𝘮 𝘲𝘶𝘢𝘯𝘵𝘶𝘮 𝘤𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨 𝘣𝘦 𝘣𝘭𝘦𝘯𝘥𝘦𝘥 𝘪𝘯𝘵𝘰 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘱𝘰𝘸𝘦𝘳𝘧𝘶𝘭 𝘔𝘓 𝘮𝘰𝘥𝘦𝘭𝘴? 🤔 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝗻 #𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀: Transf...

@jannisblrn.bsky.social wrote a very nice teaser about our Neurips paper Quantum Doubly Stochastic Transformers (spotlight). Our co-authors Filip and Kahn will present it in San Diego, and Jannis in EurIPS. You can find links to the paper, video, and poster below:

www.linkedin.com/posts/jannis...

🤓 Open position at IBM Research Zurich!
Passionate about AI for maths & curious about Quantum Computing?
Join our team & help to shape the future of computing!
We are offering internships & master theses. If you are looking for a PhD, please apply to the same ad!
👉 www.zurich.ibm.com/careers/2025...

After several years of usage by the open-source community, our paperscraper package finally has its own Docs available: jannisborn.github.io/paperscraper/
Use #paperscraper for publication keyword search, download PDFs, extract citation statistics and many more! 🚀

Check out our workflow for AI-driven molecular design. We’ve successfully validated this experimentally already (papers coming soon)!

Potential role of developmental experience in the emergence of the parvo-magno distinction - Communications Biology Developmentally-driven computational modeling study suggests that early sensory experience shapes distinct neuronal response properties in the visual system, providing a potential account of the emerg...

1/ New paper out in @commsbio.nature.com, led by @marinv.bsky.social: doi.org/10.1038/s420...! Across several past studies, we showed how newborns' degraded vision may benefit human development and inspire more robust deep networks. We have referred to this as Adaptive Initial Degradations (AID).

GitHub - tum-ai/number-token-loss: A regression-alike loss to improve numerical reasoning in language models A regression-alike loss to improve numerical reasoning in language models - tum-ai/number-token-loss

Jonas Zausinger*, Lars Pennig*, Anamarija Kozina, Sean Sdahl, Julian Sikora, Adrian Dendorfer, Timofey Kuznetsov, Mohamad Hagog, Nina Wiedemann, Kacper Chlodny, Vincent Limbach, Anna Ketteler, Thorben Prein, Vishwa Mohan Singh & Michael Danziger.

💻 GitHub code: ibm.biz/ntl-code

It was an incredible experience to run this project 🚀 But it only really came to life through the endless effort of all the amazing co-authors 🔥💪

🌐 Landing page: ibm.biz/ntl-main

5. Text-task friendly: Doesn’t interfere with CE on purely textual tasks 📚
6. Scalable: Tested up to 3B, e.g., with hashtag#IBMGranite 3.2🚀
7. Plug-and-play: It’s “just a loss,” so it’s super easy to adopt 🔢
📄 ICML paper: ibm.biz/ntl-paper

1. Better math performance: NTL consistently boosts accuracy on math benchmarks (e.g., GSM-8K) 📊
2. Lightning-fast: 100× faster to compute than CE, so there’s no training overhead ⚡
3. Model-agnostic: Works with Transformers, Mamba, etc. 🤖
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🎛️ Hugging Face Spaces demo: ibm.biz/ntl-demo

In our upcoming #ICML2025 paper, we introduce the #NumberTokenLoss (NTL) to address this -- see the demo above! NTL is a regression-style loss computed at the token level—no extra regression head needed. We propose adding NTL on top of CE during LLM pretraining. Our experiments show: (see ⬇️ )

#ICML Why are LLMs so powerful but still suck at math? 🤔 A key problem is cross-entropy loss: It is nominal-scale, so tokens are unordered. That makes sense for words, but not for numbers. For a "5" label, predicting “6” or “9” gives the same loss 😱 Yes, it's crazy! No, nobody has fixed this yet! ⬇️

Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap Learning the response of single-cells to various treatments offers great potential to enable targeted therapies. In this context, neural optimal transport (OT) has emerged as a principled methodologic...

🚨 Our new paper: Conditional Optimal Transport generalizes well to unseen drugs. Big step forward, thanks to conditional Monge Gap! Even better: conditional models often beat local, non-conditional ones. arxiv.org/abs/2504.08328. Code public! Thanks to all co-authors
@marianna-raps.bsky.social

Great to hear! 🙃 Let me know if there are questions

Our next journal club meeting will be discussing "A Computational Investigation of Inventive Spelling and the 'Lesen durch Schreiben' Method" by @jannisblrn.bsky.social et al. on 23 Jan 2025, 11am - 12pm (GMT+1). Join us by emailing us at gewonn.contact.us@gmail.com, and stay tuned for more news!

If you're @neuripsconf.bsky.social and into #OptimalTransport & bio, dont miss on Alice Driessen's spotlight talk on #ConditionalMongeGap for modeling CAR Response. Today #AIDrugX workshop!

Positive results on OOD perturbations -> accurate gene expression prediction. Paper: ibm.biz/carot-pre

Full poster

Number token loss

A new loss improves math capabilities in language models! The loss is model-agnostic and only requires to know which tokens represent numbers.
No computational overhead but better performance.
Poster today @NeurIPS - MathAI Workshop! Thx to collaborators from TUM AI!
Paper: arxiv.org/abs/2411.02083

Can we iteratively design small molecules with desired target properties, simply by sending messages on Slack? YES!

Super excited to give a live demo on🤖dZiner🧪 during the SPOTLIGHT 🔦 talk at #AI4Mat #NeurIPS2024!

Preprint: lnkd.in/e-24AEHC
Code: lnkd.in/egF4hGCg