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#ai4science

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UKP Lab

Also consider following the authors Aniket Pramanick (Ubiquitous Knowledge Processing (UKP) Lab)‬, Yufang Hou (IT:U- Interdisciplinary Transformation University Austria, IBM Research), Saif M Mohammad (National Research Council Canada / Conseil national de recherches Canada), and Iryna Gurevych (Ubiquitous Knowledge Processing (UKP) Lab).

🗺️ See you at #ACL2025 in Vienna

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#NLProc#AI4Science
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MottG

(copied from post on X by Jure Leskovec)

Announcing Biomni — the first general-purpose biomedical AI agent. Biomni is a free web platform where biomedical scientists can immediately delegate their tasks to Biomni.

Biomni is an open-source initiative: we invite the community to build on it and advance biomedical research at scale.
- Try it now: biomni.stanford.edu
- Paper: biomni.stanford.edu/paper.pdf
- Code: github.com/snap-stanford/biomn

#research #medicine #AI4Science
#AItools #bioinformatics

Dr. Chris Rackauckas :julia:

New blog post: Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism.

stochasticlifestyle.com/machin

Stochastic Lifestyle · Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism - Stochastic LifestyleWe recently released a new manuscript Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints where we showed a way to develop neural networks where any arbitrary constraint function can be directly imposed throughout the evolution equation to near floating point accuracy. However, in true academic form it focuses directly on getting to the point about the architecture, but here I want to elaborate about the mathematical structures that surround the object, particularly the differential-algebraic equation (DAE), how its various formulations lead to the various architectures (such as stabilized neural ODEs), and elaborate on the other related architectures which haven’t had a paper yet but how you’d do it (and in what circumstances they would make sense).
#sciml#ai4science#hardconstraints
Dr. Chris Rackauckas :julia:

New blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.

stochasticlifestyle.com/how-ch

Stochastic Lifestyle · How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims - Stochastic LifestyleJust how chaotic are chaotic systems? Many of you may have heard of “the butterfly effect” but don’t quite know the mathematics behind such systems. What I want to demonstrate is the “sensitive dependence to initial conditions” property of chaotic systems and just how sensitive these systems are. The reason this has come up is that I have seen some AI papers claiming to be able to predict the timeseries of a chaotic system (many more can be found online too, just highlighting a few random ones). What I want to bring to the forefront is an examination of what is really being claimed: just how hard is it to actually forecast a chaotic system? And if they aren’t doing that, what have they done instead? Quick Understanding of Chaos: Sensitive Dependence and the Shadowing Lemma First of ... READ MORE
#julialang#sciml#chaos
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Elise van der Pol

Interested in the unique challenges at the intersection of Reinforcement Learning & Drug Discovery?

New internship in Amsterdam: jobs.careers.microsoft.com/glo

Do research with Krzysztof Maziarz, Guoqing Liu, José Garrido-Torres, Ran Bi, Marwin Segler & me in AI4Science!

Questions? Reach out!

jobs.careers.microsoft.comInternship Opportunities: AI4Science Research Internship - Reinforcement Learning & Drug Discovery in Amsterdam, Noord-Holland, Netherlands | Research, Applied, & Data Sciences at MicrosoftApply for Internship Opportunities: AI4Science Research Internship - Reinforcement Learning & Drug Discovery in Amsterdam, Noord-Holland, Netherlands | Research, Applied, & Data Sciences at Microsoft
#Reinforcementlearning#ai4science
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