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Reposted by Guy Moss

Machine Learning in Science @mackelab.bsky.social · 7d

Congrats to Dr Michael Deistler @deismic.bsky.social, who defended his PhD!

Michael worked on "Machine Learning for Inference in Biophysical Neuroscience Simulations", focusing on simulation-based inference and differentiable simulation.

We wish him all the best for the next chapter! 👏🎓

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Reposted by Guy Moss

Machine Learning in Science @mackelab.bsky.social · 9d

The Macke lab is well-represented at the @bernsteinneuro.bsky.social conference in Frankfurt this year! We have lots of exciting new work to present with 7 posters (details👇) 1/9

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Reposted by Guy Moss

Richard Gao @rdgao.bsky.social · 16d

I've been waiting some years to make this joke and now it’s real:

I conned somebody into giving me a faculty job!

I’m starting as a W1 Tenure-Track Professor at Goethe University Frankfurt in a week (lol), in the Faculty of CS and Math

and I'm recruiting PhD students 🤗

a man wearing a white shirt and tie smiles in front of a window

ALT: a man wearing a white shirt and tie smiles in front of a window

media.tenor.com

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Reposted by Guy Moss

sbi - Simulation-based inference @sbi-devs.bsky.social · Sep 9

From hackathon to release: sbi v0.25 is here! 🎉

What happens when dozens of SBI researchers and practitioners collaborate for a week? New inference methods, new documentation, lots of new embedding networks, a bridge to pyro and a bridge between flow matching and score-based methods 🤯

1/7 🧵

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Reposted by Guy Moss

psteinb.bsky.social @psteinb.bsky.social · Aug 19

Looky Looky! 😍🥳👏
arxiv.org/abs/2508.12939
Super fun project, I ❤️ed coauthoring w/ @sbi-devs.bsky.social.
Great lead by @deismic.bsky.social & @janboelts.bsky.social. Contribs by many talented people @jakhmack.bsky.social. 🙏 to #BenjaminKurtMiller for the kickstart! @helmholtzai.bsky.social

Simulation-Based Inference: A Practical Guide

A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framewo...

arxiv.org

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Reposted by Guy Moss

Machine Learning in Science @mackelab.bsky.social · Jul 23

New preprint: SBI with foundation models!
Tired of training or tuning your inference network, or waiting for your simulations to finish? Our method NPE-PF can help: It provides training-free simulation-based inference, achieving competitive performance with orders of magnitude fewer simulations! ⚡️

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Reposted by Guy Moss

Jakob Macke @jakhmack.bsky.social · Jul 23

I have been genuinely amazed how well tabpfn works as a density estimator, and how helpful this is for SBI ... Great work by @vetterj.bsky.social, Manuel and @danielged.bsky.social!!

Machine Learning in Science @mackelab.bsky.social · Jul 23

New preprint: SBI with foundation models!
Tired of training or tuning your inference network, or waiting for your simulations to finish? Our method NPE-PF can help: It provides training-free simulation-based inference, achieving competitive performance with orders of magnitude fewer simulations! ⚡️

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Reposted by Guy Moss

Daniel Gedon @danielged.bsky.social · Jul 23

My first paper on simulation-based inference (SBI) as part of @mackelab.bsky.social!

Exciting work on adapting state-of-the-art foundation models for posterior estimation. Almost plug-and-play, and surprisingly effective.

Paper/code in thread below 🧵

Machine Learning in Science @mackelab.bsky.social · Jul 23

New preprint: SBI with foundation models!
Tired of training or tuning your inference network, or waiting for your simulations to finish? Our method NPE-PF can help: It provides training-free simulation-based inference, achieving competitive performance with orders of magnitude fewer simulations! ⚡️

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Reposted by Guy Moss

Geophysics Tübingen @geophys-tuebingen.bsky.social · Jun 23

New paper in Geophysical Research Letters led by Vjeran Višnjević mapping out ice shelf areas which are maintained by local precipitation only doi.org/10.1029/2024...

Mapping the Composition of Antarctic Ice Shelves as a Metric for Their Susceptibility to Future Climate Change

We categorize Antarctic ice shelves into two parts: local meteoric ice and continental meteoric ice Buttressed ice shelves composed primarily of local meteoric ice are identified as being particu...

doi.org

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Guy Moss @gmoss13.bsky.social · Jun 11

Have I been to Antarctica? No. But my colleagues have, and we can learn a lot from the data they collected! Really happy to share that our work is now published!

Machine Learning in Science @mackelab.bsky.social · Jun 11

Thrilled to share that our paper on using simulation-based inference for inferring ice accumulation and melting rates for Antarctic ice shelves is now published in Journal of Glaciology!

www.cambridge.org/core/journal...

Simulation-based inference of surface accumulation and basal melt rates of an Antarctic ice shelf from isochronal layers | Journal of Glaciology | Cambridge Core

Simulation-based inference of surface accumulation and basal melt rates of an Antarctic ice shelf from isochronal layers - Volume 71

www.cambridge.org

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Reposted by Guy Moss

sbi - Simulation-based inference @sbi-devs.bsky.social · May 20

More great news from the SBI community! 🎉
Two projects have been accepted for Google Summer of Code under the NumFOCUS umbrella, bringing new methods and general improvements to sbi. Big thanks to @numfocus.bsky.social, GSoC and our future contributors!

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sbi - Simulation-based inference @sbi-devs.bsky.social · May 12

Great news! Our March SBI hackathon in Tübingen was a huge success, with 40+ participants (30 onsite!). Expect significant updates soon: awesome new features & a revamped documentation you'll love! Huge thanks to our amazing SBI community! Release details coming soon. 🥁 🎉

A wide shot of approximately 30 individuals standing in a line, posing for a group photograph outdoors. The background shows a clear blue sky, trees, and a distant cityscape or hills.

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Machine Learning in Science @mackelab.bsky.social · Apr 30

🎓Hiring now! 🧠 Join us at the exciting intersection of ML and Neuroscience! #AI4science
We’re looking for PhDs, Postdocs and Scientific Programmers that want to use deep learning to build, optimize and study mechanistic models of neural computations. Full details: www.mackelab.org/jobs/ 1/5

Jobs - mackelab

The MackeLab is a research group at the Excellence Cluster Machine Learning at Tübingen University!

www.mackelab.org

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Reposted by Guy Moss

Machine Learning in Science @mackelab.bsky.social · Apr 25

Excited to present our work on compositional SBI for time series at #ICLR2025 tomorrow!

If you're interested in simulation-based inference for time series, come chat with Manuel Gloeckler or Shoji Toyota

at Poster #420, Saturday 10:00–12:00 in Hall 3.

📰: arxiv.org/abs/2411.02728

Compositional simulation-based inference for time series

Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likelihoods, it often requir...

arxiv.org

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Reposted by Guy Moss

psteinb.bsky.social @psteinb.bsky.social · Apr 11

🥳Great news, our JOSS paper "sbi reloaded" has been accepted! 🎉
This community lead by the fine folks of @sbi-devs.bsky.social is very welcoming and super fun to work with! I learn with every discussion I have.
paper: joss.theoj.org/papers/10.21...
review: github.com/openjournals...

[REVIEW]: sbi reloaded: a toolkit for simulation-based inference workflows · Issue #7754 · openjournals/joss-reviews

Submitting author: @janfb (Jan Boelts) Repository: https://github.com/sbi-dev/sbi Branch with paper.md (empty if default branch): joss-submission-2024 Version: v0.24.0 Editor: @boisgera Reviewers: ...

github.com

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Reposted by Guy Moss

psteinb.bsky.social @psteinb.bsky.social · Mar 21

It's been a blast, thanks to @sbi-devs.bsky.social ! This week's hackathon was phenomenal! 🙏 😍 The sbi hackathon welcomed about 25 people in Tübingen with contributions spanning the globe , e.g. 🇺🇸🇯🇵🇧🇪🇩🇪. Wanna see, what we did? Check out the PRs👇
github.com/sbi-dev/sbi/...

Pull requests · sbi-dev/sbi

sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has ...

github.com

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Reposted by Guy Moss

sbi - Simulation-based inference @sbi-devs.bsky.social · Jan 28

🙏 Please help us improve the SBI toolbox! 🙏

In preparation for the upcoming SBI Hackathon, we’re running a user study to learn what you like, what we can improve, and how we can grow.

👉 Please share your thoughts here: forms.gle/foHK7myV2oaK...

Your input will make a big difference—thank you! 🙌

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Reposted by Guy Moss

sbi - Simulation-based inference @sbi-devs.bsky.social · Jan 14

🚀 Join the 4th SBI Hackathon! 🚀
The last SBI hackathon was a fantastic milestone in forming a collaborative open-source community around SBI. Be part of it this year as we build on that momentum!

📅 March 17–21, 2025
📍 Tübingen, Germany or remote
👉 Details: github.com/sbi-dev/sbi/...

More Info:🧵👇

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Reposted by Guy Moss

sbi - Simulation-based inference @sbi-devs.bsky.social · Dec 30

🎉 Just in time for the end of the year, we’ve released a new version of sbi!

📦 v0.23.3 comes packed with exciting features, bug fixes, and docs updates to make sbi smoother and more robust. Check it out! 👇

🔗 Full changelog: github.com/sbi-dev/sbi/...

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Reposted by Guy Moss

Arnaud Doucet @arnauddoucet.bsky.social · Dec 15

The slides of my NeurIPS lecture "From Diffusion Models to Schrödinger Bridges - Generative Modeling meets Optimal Transport" can be found here
drive.google.com/file/d/1eLa3...

BreimanLectureNeurIPS2024_Doucet.pdf

drive.google.com

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Reposted by Guy Moss

Auguste Schulz @auschulz.bsky.social · Dec 11

1) With our @neuripsconf.bsky.social poster happening tomorrow, it's about time to introduce our Spotlight paper 🔦, co-lead with @jkapoor.bsky.social:

Latent Diffusion for Neural Spiking data (LDNS), a latent variable model (LVM) which addresses 3 goals simultaneously:

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Reposted by Guy Moss

Matthijs Pals @matthijspals.bsky.social · Dec 11

How to find all fixed points in piece-wise linear recurrent neural networks (RNNs)?
A short thread 🧵
 In RNNs with N units with ReLU(x-b) activations the phase space is partioned in 2^N regions by hyperplanes at x=b 1/7

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Guy Moss @gmoss13.bsky.social · Dec 10

Interested to learn more? Come visit our poster at #Neurips2024, or simply get in touch! Huge thanks again to @vetterj.bsky.social , Cornelius Schröder, @rdgao.bsky.social , and @jakhmack.bsky.social
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Guy Moss @gmoss13.bsky.social · Dec 10

We apply Sourcerer to a real dataset of single-neuron recordings and the Hodgkin-Huxley model. This model is misspecified and highly nonlinear. Still, Sourcerer estimates source distributions that accurately reproduce the dataset, again achieving higher entropy “for free”!
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Guy Moss @gmoss13.bsky.social · Dec 10

With the likelihood-free loss, Sourcerer can make use of differentiable simulators to quickly estimate source distributions, even when the data is high-dimensional, such as for time series data.
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