Jonas Fischer

84 posts

Jonas Fischer

Jonas Fischer

@JonasFischerML

Research group leader @ Max Planck Institute for Informatics | Interested in ML, Stats, Data Mining, and everything that connects them.

Katılım Temmuz 2022
144 Takip Edilen136 Takipçiler
Jonas Fischer retweetledi
Jan Eric Lenssen
Jan Eric Lenssen@janericlenssen·
Can 3D scenes be represented by and rendered from a set of compressed tokens? It turns out they can and it pairs very well with generative rendering to handle uncertainty! Make sure to check out @Mohamma68780050's recent work Scenetok, accepted at #CVPR2026. Links below.
GIF
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Fawaz Sammani
Fawaz Sammani@fawaz_sammani·
Our paper “CLIP-Free, Label-Free, Unsupervised Concept Bottleneck Models” has been accepted to #CVPR2026 (Findings). This is one of my strongest PhD works, but at the same time, the most unluckiest. Anyways, Im happy to see its out now! #xai Paper: arxiv.org/pdf/2503.10981
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Chris Olah
Chris Olah@ch402·
We're hiring ~10 research engineers on the Interpretability team at Anthropic. If you're a seasoned ML infra engineer who's excited about understanding what's happening inside frontier models, we'd love to hear from you. (No prior interp experience needed!)
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Ada Görgün
Ada Görgün@AdaGorgun·
Diffusion models create stunning images, but when and how do specific concepts actually emerge during the denoising process? 🎉 Excited to share that our paper “Temporal Concept Dynamics in Diffusion Models via Prompt-Conditioned Interventions” has been accepted to ICLR 2026! 😊
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Nhi Pham
Nhi Pham@NhiPham49·
Robust classifiers are great. Interpretable models are great. But why do we still have to choose between the two? 🎉 Excited to share our paper "Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning" has been accepted to #ICLR2026 @iclr_conf !
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Explainable Machine Learning
Explainable Machine Learning@ExplainableML·
🎓PhD application season @ExplainableML is back! This year, we’re hiring ONLY through the ELLIS Program and the MCML Program: two fantastic communities shaping the future of AI and ML. 📌Please don’t forget to denote Prof. Dr. Zeynep Akata as one of your preferred supervisors! 👉 Link to ELLIS (ellis.eu/news/ellis-phd…) and MCML (mcml.ai/opportunities/…)
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Zeynep Akata
Zeynep Akata@zeynepakata·
We are looking for PhD students. Please apply through the @ELLISforEurope and @MunichCenterML PhD programs!
Explainable Machine Learning@ExplainableML

🎓PhD application season @ExplainableML is back! This year, we’re hiring ONLY through the ELLIS Program and the MCML Program: two fantastic communities shaping the future of AI and ML. 📌Please don’t forget to denote Prof. Dr. Zeynep Akata as one of your preferred supervisors! 👉 Link to ELLIS (ellis.eu/news/ellis-phd…) and MCML (mcml.ai/opportunities/…)

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Naomi Saphra
Naomi Saphra@nsaphra·
I’m recruiting PhD students for 2026! If you are interested in robustness, training dynamics, interpretability for scientific understanding, or the science of LLM analysis you should apply. BU is building a huge LLM analysis/interp group and you’ll be joining at the ground floor.
Naomi Saphra@nsaphra

Life update: I'm starting as faculty at Boston University in 2026! BU has SCHEMES for LM interpretability & analysis, so I couldn't be more pumped to join a burgeoning supergroup w/ @najoungkim @amuuueller. Looking for my first students, so apply and reach out!

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Thomas Wimmer
Thomas Wimmer@wimmer_th·
Super excited to introduce ✨ AnyUp: Universal Feature Upsampling 🔎 Upsample any feature - really any feature - with the same upsampler, no need for cumbersome retraining. SOTA feature upsampling results while being feature-agnostic at inference time.
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Sukrut Rao
Sukrut Rao@sukrutrao·
Are you an XAI researcher attending #ICCV2025? Submit your recently published work — at CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, AAAI etc. — to the eXCV Workshop for the opportunity to further showcase your work! Published papers can be submitted as is, no rewriting necessary.
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Nina Shvetsova
Nina Shvetsova@ninashv__·
🚀 UTD is now fully released! Code ✅ Models ✅ 2M video descriptions ✅ Debiased splits for 12 datasets ✅ Everything you need to benchmark video models more fairly is now public: 🔗 github.com/ninatu/utd-pro… 🎥 Let’s make video understanding actually about video understanding.
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tender
tender@tenderizzation·
when you rely on data augmentation because your model doesn’t have scale equivariance built-in
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Michael Kamp
Michael Kamp@MichaelKamp7·
PhD Position in Trustworthy AI at the Lamarr Institute (TU Dortmund, Germany)! I’m looking for outstanding PhD student working on: ✅ Federated & Multi-Agent Learning ✅ Theory of Deep Learning ✅ Causality and Causal Representations (1/7)
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Keşfet

@Mohamma68780050 @nobolly @AdaGorgun @fawaz_sammani @NhiPham49 @AJesslen @AdamKortylewski @iclr_conf