Real data is noisy but HiPPO assumes it's clean. Our UnHiPPO initialization resists noise with implicit Kalman filtering and makes SSMs robust without architecture changes.
Learn more at our #ICML poster: Thu 11am E-2409
Paper: openreview.net/forum?id=U8G...
Code: github.com/martenlienen...
I am truly excited to share our latest work with @mscherbela.bsky.social, Philipp Grohs, and @guennemann on "Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems"!
arxiv.org/abs/2504.06087
martenlienen.com/blog/what-is...
A summary of my basic understanding of SDEs, Ito and Stratonovich integrals that I gathered from the great book Applied Stochastic Differential Equations
#math #machinelearning
I just asked #gemini 2.0 flash thinking to do some #math for me and this is the first time that I have seen an #LLM refer to its own thought process / thinking tokens without "realizing" that I would not see those.
We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, Fi...
BioEmu now published in @science.org !!
What is BioEmu? Check out this video:
youtu.be/LStKhWcL0VE?...
Marten Lienen
Nicholas Gao
Marten Lienen
Marten Lienen
Frank Noe
Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system that...
