Johannes Zenn @johanneszenn
PhD Student at University of Tübingen, mostly active on Bluesky https://t.co/2LZiVXKA4z jzenn.github.io Deutschland Joined April 2013-
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[FridayTalks@Tübingen] New recording is online! Title: Large Language Models Are Zero-Shot Problem Solvers—Just Like Modern Computers Speaker: @TimZXiao youtu.be/ySHuahiazD4?si…
New Recording of Friday Talks @ Tübingen is online! Title: Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models Speakers: Julius Vetter & Manuel Glöckler @gloecklermanuel youtu.be/Wx2p1rrggmM?si…
🧠 Two years ago we wrote a blog post exploring an analogy: Large Language Models Are Zero-Shot Problem Solver —Just Like Modern Computers Today, that idea is published on Harvard Data Science Review @TheHDSR ! 🔗 hdsr.mitpress.mit.edu/pub/42vjucmq/
📢 [Openings] I'm now an Assistant Prof @WesternU CS dept. Funded PhD & MSc positions available! Topics: large probabilistic models, decision-making under uncertainty, and apps in AI4Science. More on agustinus.kristia.de/openings/
Great paper by my students @TimZXiao and @johanneszenn and collaborators that applies ideas from Monte Carlo sampling to (black-box) LLM execution to turn LLMs into better calibrated stochastic samplers.
Great paper by my students @TimZXiao and @johanneszenn and collaborators that applies ideas from Monte Carlo sampling to (black-box) LLM execution to turn LLMs into better calibrated stochastic samplers.
✨ New paper: Flipping Against All Odds We found that large language models (LLMs) can describe probabilities—but fail to sample from them faithfully. Yes, even flipping a fair coin is hard. 🪙 🧵 Here’s what we learned—and how we fixed it. 🔗arxiv.org/abs/2506.09998 1/
I'm extremely disappointed with the organization of the upcoming German election. Voting is a fundamental right, yet my ballot letter arrived too late in Toronto—now the fastest! delivery won't get it there by Monday, after the election. Unacceptable. @Wahlleitung_de @GermanyinTO
𝟏/ 🚨Attention NeurIPS attendees! 🚨 Isotropic weight space priors are sabotaging your Laplace approximation to your neural network’s posterior, and we’ve got the fix. Curious? Read on 🧵
1) With our @NeurIPSConf poster happening tomorrow, it's about time to introduce our Spotlight paper 🔦, co-lead with @_Jaivardhan_ : Latent Diffusion for Neural Spiking data (LDNS), a latent variable model (LVM) which addresses 3 goals simultaneously:
Latent Diffusion for Neural Spiking Data -- a flexible generative model for realistic neural recordings co-led with @SchulzAuguste! This was a very fun and intense project, and my first (and hopefully not the last) foray into #neuro! Check out the thread - lots of pretty plots…
Latent Diffusion for Neural Spiking Data -- a flexible generative model for realistic neural recordings co-led with @SchulzAuguste! This was a very fun and intense project, and my first (and hopefully not the last) foray into #neuro! Check out the thread - lots of pretty plots…
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
I am super hyped and happy with our recent paper on a ✨VampPrior 2.0✨: Hierarchical VAE with a diffusion-based VampPrior 🦇 We got SOTA VAE results! Kudos to @a_kzna, this TMLR paper is the last chapter in her PhD thesis 🤩 📃 tinyurl.com/22rvzc4f 💻 tinyurl.com/3ddrbfcj
@SchulzAuguste presenting our recently accepted #NeurIPS #spotlight paper on Latent Diffusion for Neural Spiking Data, check out the poster if you're at @BernsteinNeuro! 🧠
@SchulzAuguste presenting our recently accepted #NeurIPS #spotlight paper on Latent Diffusion for Neural Spiking Data, check out the poster if you're at @BernsteinNeuro! 🧠
Researching #AI #MachineLearning #Robotics #HCI? Join our elite doctoral program - a partnership with MPI-IS, @Uni_Stuttgart & @uni_tue! Applications accepted until Nov 15, 2024 at imprs.is.mpg.de/application
Biggest joy and honour leading this project at the intersection of visual neuroscience and ML to a successful finish! Paper: nature.com/articles/s4158…
How could online learning apps adapt to learners and improve over time? Even if you're not a machine learning expert, @hanqizh's blog post on our last ICLR paper explains new approaches in simple terms (joint work with @alvorithm and @TheCharleyWu, supported by @TheresaAuthaler).
🚀 Excited to introduce our new work: SGP-Bench! Can Large Language Models (LLMs) understand symbolic graphics programs? 🖥️ Imagine giving a model a symbolic graphics program like SVG or CAD and asking it to answer questions about the visual content without actually seeing the…
Want to find out more about Verbalized Machine Learning? Come and talk to @Besteuler and I at the ICL workshop tomorrow (Saturday)!😊 #ICML2024 Poster session: 10:25 - 11:15 AM 3:20 - 4:15 PM Github🔗: github.com/timxzz/VML_Exa…
Want to find out more about Verbalized Machine Learning? Come and talk to @Besteuler and I at the ICL workshop tomorrow (Saturday)!😊 #ICML2024 Poster session: 10:25 - 11:15 AM 3:20 - 4:15 PM Github🔗: github.com/timxzz/VML_Exa… https://t.co/U9Z20NMOOi
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GRaM Workshop at ICML... @GRaM_org_
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Guray O. @hutcun
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JHU CLSP @jhuclsp
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Johns Hopkins Univers... @JohnsHopkins
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Bloomberg @business
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