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Florentin Guth

@florentinguth.bsky.social
186 followers 118 following 20 posts

Postdoc at NYU CDS and Flatiron CCN. Wants to understand why deep learning works.

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Florentin Guth @florentinguth.bsky.social · 06/06/2025
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.
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Florentin Guth @florentinguth.bsky.social · 06/06/2025
High probability ≠ typicality: very high-probability images are rare. This is not a contradiction: frequency = probability density *multiplied by volume*, and volume is weird in high dimensions! Also, the log probabilities are Gumbel-distributed, and we don't know why!
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Florentin Guth @florentinguth.bsky.social · 06/06/2025
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! 🤯
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Florentin Guth @florentinguth.bsky.social · 06/06/2025
But how do we know our probability model is accurate on real data? In addition to computing cross-entropy/NLL, we show *strong* generalization: models trained on *disjoint* subsets of the data predict the *same* probabilities if the training set is large enough!
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Florentin Guth @florentinguth.bsky.social · 06/06/2025
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.
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Florentin Guth @florentinguth.bsky.social · 06/06/2025
We also propose a simple procedure to obtain good network architectures for the energy U: choose any pre-existing score network s and simply take the inner product with the input image y! We show that this preserves the inductive biases of the base score network: grad_y U ≈ s.
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Florentin Guth @florentinguth.bsky.social · 06/06/2025
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).
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Florentin Guth @florentinguth.bsky.social · 06/06/2025
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: 🧵
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