Frank Schäfer
@_Frank_Schaefer
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Researcher @ Axiomatic AI || Previously: @Extropic_AI, @MIT, @MIT_CSAIL, @UniBasel, @UniFreiburg
Cambridge, MA
Joined July 2022
Fine-tuning an LLM on a narrowly harmful dataset can lead it to develop broadly misaligned behavior - that's emergent misalignment. In a new preprint https://t.co/eX1NDCaT9S, we decompose this fine-tuning transition, quantifying what behavioral aspects of the model really change!
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If you're interested in a PhD at the intersection of machine learning and programming languages, consider Yale CS! We're exploring new ways to build software that draws inferences & makes predictions. See https://t.co/uFSVhlBNvT & apply at https://t.co/pPCQps7Jch by Dec. 15 😃
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We are organizing a workshop on ML for quantum matter in Dresden in February 2025. Amazing speaker lineup. The application deadline is Nov. 30, apply!
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"Differentiable Programming for Differential Equations: A Review" (by Facundo Sapienza, Jordi Bolibar, Frank Schäfer, Brian Groenke, Avik Pal, Victor Boussange, Patrick Heimbach, Giles Hooker, Fernando Pérez, Per-Olof Persson, Christopher Rackauckas):
arxiv.org
The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Many scientific...
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New heat coming out of Gothenburg by my (almost) next-door neighbour @MoritzSchauer and collaborators:
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🚨 Preprint alert 🚨 Excited to share this review paper, after a massive effort led by @SapienzaFacu. We hope this will help advance the fusion of scientific models and data through differentiable programming. 👇 https://t.co/sLlYIk4jX8
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1/6 Surrogate gradients (SGs) are empirically successful at training spiking neural networks (SNNs). But why do they work so well, and what is their theoretical basis? In our new preprint led by @JuliaGygax4, we provide the answers: https://t.co/QkQ4MniGIG
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#PRLtrending for the week of 2024-05-28 #physics #trending
https://t.co/KAgysWSS4Q
https://t.co/WJt9T7RMnm
#open
https://t.co/gLyoY3vSH5
https://t.co/MZAG3Q4TPp
#open
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Check out https://t.co/S4CikGArUe with @FlemmingHoltorf & @_Frank_Schaefer (@MIT_CSAIL) and Niels Lörch (@UniBasel): Leveraging tools from physics, we analyze phase transitions in LLMs. These mark abrupt changes in behavior as prompt, training epoch, or temperature are varied.
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I'm excited to share that our recent work together with @_Frank_Schaefer on automating the process of mapping out phase diagrams is out in PRL https://t.co/KvCeyl4vZA! 1/2
journals.aps.org
One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To...
Want to map out phase diagrams of physical systems in an automated fashion? In our new preprint, we demonstrate a novel way how this can be done on the basis of generative models https://t.co/LdrGzm6ACi
@UniBasel @MIT @MIT_CSAIL 1/5
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4-year PhD position in Statistics at the Department of Mathematics of Vrije Universiteit Amsterdam. The position is part of the EU-funded Beyond The Edge Doctoral Network ( https://t.co/NzZuYcoNcc).
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Simulating conditioned diffusions on manifolds https://t.co/qMgb3UXDrX with Marc Corstanje, @MeulenFrank and @MoritzSchauer. We construct a method for simulating diffusion bridges on manifolds by extending the notion of guided processes to manifolds, replacing the h-function in
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Ever wondered how machines detect phase transitions from data? In our new preprint, we unveil a deep connection between machine-learned indicators of phase transitions and the Fisher information https://t.co/C7QPJTR4Yr
@UniBasel @MIT @MIT_CSAIL 1/
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New open source tool from @JuliaHub_Inc: static code analysis to prove that a #julialang code is allocation-free. Use this to ensure that codes are safe for real-time applications, such as how we use it for JuliaSim to analyze #SciML control codes! https://t.co/9MpQzgLGqS
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Happy to announce that this work in collaboration with @_Frank_Schaefer (@MIT @MIT_CSAIL) and Niels Lörch (@UniBasel) has been accepted at the NeurIPS 2023 "Machine Learning and the Physical Sciences" workshop https://t.co/vXIAsxyMCv. Niels and I hope to see you there in person!
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We're hiring research scientists and engineers in programming languages, probabilistic machine learning & causal inference Apply/dm me if you want to build general reasoning systems with solid foundations, and use them to solve hard scientific & societal problems RT appreciated
We’re hiring! Basis’ vision is to build a universal reasoning engine to advance science and solve problems of societal importance. We're expanding our team and have multiple positions open: internships, postdoc fellowships, and full-time roles. Details👇 https://t.co/Btyj5GzCWq
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My talk at the #JuliaCon 2023 on ``Differentiable isospectral flows for matrix diagonalization'' is now online https://t.co/ZMvWDOyxpH. Check it out!
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The saga on differentiable HEP continues: new Paper by @Michael_A_Kagan and myself on Differentiable Showers: https://t.co/RglJnYj0Be We explore a few gradient estimation techniques, including the new StochasticAD by @NotGauravArya et al.
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A recent blog post by @GamsSoftware demonstrated a significant performance difference between JuMP and GAMS. We respond by explaining the difference in performance and presenting an alternative JuMP implementation with asymptotically better performance. https://t.co/vvJ84sCCSY
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