Greg Ver Steeg

[Tamoghna Chattopadhyay @TamoghnaChatto2]

✨🧠Excited to have had the opportunity to present our work titled 'Diffusion Bridge Models for 3D Medical Image Translation' in IEEE #embc2025 conference. Some of the important points of discussion from the paper are in this 🧵

[Greg Ver Steeg @gesteller]

Your diffusion model is secretly a noise classifier https://t.co/GXIL2xrMDO

[Greg Ver Steeg @gesteller]

@CommsBio

[Greg Ver Steeg @gesteller]

https://t.co/bi9lCu6YvT Nice work on latent space learning with biomedical data by amazing PhD student Myrl Marmarelis, who is working at the intersection of causality and machine learning. He's defending soon, so you should move fast if you want to lure him somewhere!

[Xianghao Kong @xk_theo7]

1/8
🚀 AI Breakthrough: "Interpretable Diffusion via Information Decomposition" 🧠

- Quantitative understanding of conditional diffusion models.
- Align text-image data using mutual information.
- Goes beyond "attention".

🎉 Accepted at #ICLR2024!

[Rob Brekelmans @brekelmaniac]

MI + diffusion! two new papers calculating (pointwise) mutual information as a difference of scores

cool experiment by @xxk0ng @gesteller identifying areas of an image relevant to changing a prompt
https://t.co/1IwW17DXhj

see also @michiard https://t.co/8mxxBzuL0L

[Greg Ver Steeg @gesteller]

@zacharylipton “Mythos of Mechanistic Interpretability” is desperately needed. Thank you for your service.

[USC ISI @USC_ISI]

Congrats to our graduating ISIers! #Classof2023

@harhrayr is earning his #PhD in #ComputerScience, which he obtained under the supervision of his two advisors @aram_galstyan & @gesteller.

Following graduation, Hrayr will join @Google Research in NYC as a #researchscientist!

[Fayyaz Minhas @fayyazhere]

What are the #histopathology image #WSI patterns associated with gene expression in cancer? Can we discover them with #AI and use them for spatial profiling and #precision medicine?
Our preprint explores limitations & possibilities of #cpath for this.
https://t.co/F0FbLVmPnw

[Greg Ver Steeg @gesteller]

@elan_learns Interesting! It looks like openai uses trained classifiers to cut off some problematic statements. By using secrecy/subtlety it’s much harder to trip those safeguards.

[Greg Ver Steeg @gesteller]

Robnik et al's Micro-canonical Langevin Monte Carlo https://t.co/lObx74FjWH sets a new standard for sampling. Elegant energy preserving dynamics that sample from a target distribution faster and with less bias!

[Rob Brekelmans @brekelmaniac]

AM is a general approach for learning CNFs or SDEs, w/o backdrop thru dynamics in training
-natural for trajectory inference in biology, w/time snapshots given
-or generative modeling w/flexible endpoint dists or interpolating processes

(learned a ton from @k_neklyudov here :)

[Greg Ver Steeg @gesteller]

UC in 2023 https://t.co/JDoCOO0lMd

[Greg Ver Steeg @gesteller]

@adad8m @aram_galstyan Thanks for the pointer, I'm very excited about this paper. Solving the ergodicity issue is the main obstacle in making deterministic samplers practical and useful in many applications!

[Keith Burghardt @KeithComplexity]

**AI can detect new materials**
New paper with Marcin Abram, @DingrevilleRemi, and @gesteller just published in NPJ Computational Materials https://t.co/bUxO4J6Hy7
Slides borrowed from a GREAT talk @DingrevilleRemi will be presenting at a SIAM DS https://t.co/1GWza66UI5
🧵1/6

[Remi Dingreville @DingrevilleRemi]

Pattern formation is key in many physical and biological systems, but it can be hard to discern when transitions occur. We solve this challenge with self-supervised learning and neural nets to detect hierarchies of topological transitions. Details: https://t.co/OXNLvZ2Qo7

[VIMAL @VIMAL_USC_ISI]

Welcome to the official account of the Visual Intelligence and Multimedia Analytics Laboratory (#VIMAL) at @USC_ISI @USCViterbi @USC. Our research spans theoretical #AI, #ComputerVision, #misinformation #forensics, #deepfake detection and #biometrics.

[Daniel Moyer @dc_moyer]

I'm happy to announce that I'm joining @Vanderbilt_CS this fall as an Assistant Prof, building a Machine Learning and Medical Imaging group (alongside some truly wonderful faculty already there).

Please reach out if you want to collab!

[Rob Brekelmans @brekelmaniac]

We’ll be presenting our ICLR paper “Improving Mutual Information Estimation using Annealed and Energy-Based Bounds”, Tues @ 10:30am PDT/17:30 GMT

Talk: https://t.co/CxQJVKGV0N
Paper: https://t.co/ITPQoQfoDc
w/@sicong_huang @MarzyehGhassemi @gesteller @RogerGrosse @AliMakhzani

[J. AI Research-JAIR @JAIR_Editor]

New Article: "A Metric Space for Point Process Excitations" by Marmarelis, Ver Steeg and Galstyan https://t.co/JeWJrOJhlJ