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Jeremias Sulam

@jsulam.bsky.social

79 followers 138 following 18 posts

Assistant Prof. @ JHU 🇦🇷🇺🇸 Mathematics of Data & Biomedical Data Science jsulam.github.io

Posts Replies Media

Jeremias Sulam @jsulam.bsky.social · 22/07/2025

1) In practice, ProxDM can provide samples from the data distribution much faster than comparable methods based on the score like DDPM from (Ho et al, 2020) and even comparable to their ODE alternatives (which are much faster) (9/n)

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Jeremias Sulam @jsulam.bsky.social · 22/07/2025

So, in order to implement a Proximal/backward version of diffusion models, we need a (cheap!) way of solving this optimization problem, i.e. computing the proximal of the log densities at every single time step. If only there was a way… oh, in come Learned Proximal Networks (7/n)

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Jeremias Sulam @jsulam.bsky.social · 22/07/2025

What are proximal operators? You can think of them as generalizations of projection operators. For a given (proximable) functional \rho(x), its proximal is defined by the solution of a simple optimization problem: (6/n)

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Jeremias Sulam @jsulam.bsky.social · 22/07/2025

Backward discretization of diff. eqs. has been long studied (c.f. gradient descent vs proximal point method). Let’s go ahead and discretize the same SDE, but backwards! One problem: the update is defined implicitly... But it does admit a close form expression in terms of proximal operators! (5/n)

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Jeremias Sulam @jsulam.bsky.social · 22/07/2025

While elegant in continuous time, one needs to discretize the SDE to implement it in practice. In DF, this has always been done through forward discretization (e.g Euler-Maruyama), which combines a gradient step of the data distribution at the discrete time t (the *score*), and Gaussian noise: (3/n)

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Jeremias Sulam @jsulam.bsky.social · 22/07/2025

First, a (very) brief overview of diffusion models (DM). DM work by simulating a process that converts samples from a distribution (random noise) to samples from target distribution of interest. This process is modeled mathematically with a stochastic differential equation (SDE) (2/n)

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Jeremias Sulam @jsulam.bsky.social · 22/07/2025

Check this out 📢 Score-based diffusion models are powerful—but slow to sample. Could there be something better? Drop the scores, use proximals instead! We present Proximal Diffusion Models, providing a faster alternative both in theory* and practice. Here’s how it works 🧵(1/n)

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Jeremias Sulam @jsulam.bsky.social · 12/02/2025

Today, on #WomenInScience day, this paper on biomarker discovery for breast cancer, by my amazing student Zhenzhen, has just appeared in @cp-patterns.bsky.social 🎉 Her work shows how to construct fully interpretable biomarkers employing bi-level graph learning! @jhu.edu @hopkinsdsai.bsky.social

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Jeremias Sulam @jsulam.bsky.social · 11/12/2024

📣 What should *ML explanations* convey, and how does one report these precisely and rigorously? @neuripsconf.bsky.social come check Jacopo Teneggi's work on Testing for Explanations via betting this afternoon! I *bet* you'll like it :) openreview.net/pdf?id=A0HSm... @hopkinsdsai.bsky.social

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