Florentin Guth

I'm delighted to announce that I'll join MIT EECS as an Assistant Professor next Fall! I will be recruiting graduate students this cycle, please apply if you're interested in the science of deep learning! Despite the ambient doomerism, now is a great time to do research.

I'll be in #NeurIPS2025 Dec 3-7, do reach out (atconf/email) if you want to chat (see below)! I'm on the faculty job market 👀 Come say hi to @zahra-kadkhodaie.bsky.social @eerosim.bsky.social and I at our poster on energy-based models Fri 11am #3700 (thread in quoted post) bsky.app/profile/flor...

What is the probability of an image? What do the highest and lowest probability images look like? Do natural images lie on a low-dimensional manifold? In a new preprint with Zahra Kadkhodaie and @eerosim.bsky.social, we develop a novel energy-based model in order to answer these questions: 🧵

Finally, we test the manifold hypothesis: what is the local dimensionality around an image? We find that this depends both on the image and the size of the local neighborhood, and there exists images with both large full-dimensional and small low-dimensional neighborhoods.

What is the probability of an image? What do the highest and lowest probability images look like? Do natural images lie on a low-dimensional manifold? In a new preprint with Zahra Kadkhodaie and @eerosim.bsky.social, we develop a novel energy-based model in order to answer these questions: 🧵

These are the highest and lowest probability images in ImageNet64. An interpretation is that -log2 p(x) is the size in bits of the optimal compression of x: higher probability images are more compressible. Also, the probability ratio between these is 10^14,000! 🤯

What is the probability of an image? What do the highest and lowest probability images look like? Do natural images lie on a low-dimensional manifold? In a new preprint with Zahra Kadkhodaie and @eerosim.bsky.social, we develop a novel energy-based model in order to answer these questions: 🧵

We call this approach "dual score matching". The time derivative constrains the learned energy to satisfy the diffusion equation, which enables recovery of accurate and *normalized* log probability values, even in high-dimensional multimodal distributions.

What is the probability of an image? What do the highest and lowest probability images look like? Do natural images lie on a low-dimensional manifold? In a new preprint with Zahra Kadkhodaie and @eerosim.bsky.social, we develop a novel energy-based model in order to answer these questions: 🧵

How do we train an energy model? Inspired by diffusion models, we learn the energy of both clean and noisy images along a diffusion. It is optimized via a sum of two score matching objectives, which constrain its derivatives with both the image (space) and the noise level (time).

I'll be at @neuripsconf.bsky.social from Tuesday to Sunday! Feel free to reach out (Whova, email, DM) if you want to chat about scientific/theoretical understanding of deep learning, diffusion models, or more! (see below) And check out our Sci4DL workshop on Sunday: scienceofdlworkshop.github.io