Eero Simoncelli (@EeroSimoncelli) / X

Eero Simoncelli

164 posts

Eero Simoncelli

@EeroSimoncelli

Professor at NYU; Scientific Director, Ctr for Computational Neurocience, Flatiron Institute. Research in Computational Vision (neurons, perception, machines).

New York, NY

Joined February 2022

Pinned
Eero Simoncelli
@EeroSimoncelli
Feb 25
Replying to @docmilanfar
Interesting article! In complementary work we recently proved that the self-scheduled diffusion method we developed in july 2020 [arXiv:2007.13640] uses a blind denoiser to correctly sample from the learned data distribution: arXiv:2602.09639
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Eero Simoncelli
@EeroSimoncelli
Oct 25, 2025
Looking for a PhD program where you can study vision from different perspectives (computer vision, neuroscience, perception, image processing)? Consider applying to NYU! visionscience.com/pipermail/visi…
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Eero Simoncelli
@EeroSimoncelli
Oct 7, 2024
Looking for a PhD program where you can study vision from different perspectives (computer vision, neuroscience, perception, image processing)? Consider applying to NYU! visionscience.com/pipermail/visi…
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Eero Simoncelli
@EeroSimoncelli
Jul 10, 2024
These two remarkable formulas, derived for the case of additive Gaussian noise, can be generalized to many other of noise: cns.nyu.edu/~lcv/pubs/make…
Gabriel Peyré
@gabrielpeyre
Jul 8, 2024
Replying to @gabrielpeyre
Using the SURE, it is straightforward to derive Tweedie’s formula for the optimal denoiser as the score function (the gradient of the log-likelihood of the noisy data), which is used in diffusion models.
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Eero Simoncelli
@EeroSimoncelli
Jan 14, 2025
Graduate students and advanced undergraduates: Interested in a 3-month summer research internship in Computational Neuroscience @FlatironCCN Come join us in June! Application deadline 17 Jan 2025: apply.interfolio.com/159680
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Eero Simoncelli
@EeroSimoncelli
Jan 20, 2024
Summer Research Internships in Computational Neuroscience @FlatironCCN Graduate students and advanced undergraduates: Spend a summer doing research at the Flatiron Institute in NYC! Application deadline 2 Feb 2024: apply.interfolio.com/137388
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Eero Simoncelli
@EeroSimoncelli
Nov 24, 2024
The Center for Neural Science at NYU is aiming to add another computational neuroscientist to our community: apply.interfolio.com/157767 Come join us!
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Eero Simoncelli
@EeroSimoncelli
Oct 16, 2025
Semantic categories are captured in a union-of-subspaces deep within an unsupervised unconditional denoiser!
Zahra Kadkhodaie
@ZKadkhodaie
Oct 16, 2025
Diffusion models learn probability densities by estimating the score with a neural network trained to denoise. What kind of representation arises within these networks, and how does this relate to the learned density? @EeroSimoncelli @StephaneMallat and I explored this question.
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Eero Simoncelli
@EeroSimoncelli
Oct 8, 2024
Looking for a PhD program where you can study computational neuroscience? NYU has a fantastic array of researchers covering the field: groups.google.com/g/systems-neur…
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Eero Simoncelli
@EeroSimoncelli
Jun 9, 2025
Our latest work on the longstanding problem of learning a (normalized) energy model from data…
Florentin Guth
@FlorentinGuth
Jun 6, 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 @ZKadkhodaie @EeroSimoncelli, we develop a novel energy-based model in order to answer these questions: 🧵
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Eero Simoncelli
@EeroSimoncelli
Jul 23, 2024
Surprising and fun that this abstract object underlies real-world data! A more intuitive version: the 2D manifold of local orientation and phase for windowed sinusoids (“Gabor functions”) is a Klein bottle:
Peyman Milanfar
@docmilanfar
Jul 21, 2024
Images aren’t arbitrary collections of pixels -they have complicated structure, even small ones. That’s why it’s hard to generate images well. Let me give you an idea: 3×3 gray images represented as points in ℝ⁹ lie approximately on a 2-D manifold: the Klein bottle! 1/3
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Eero Simoncelli
@EeroSimoncelli
Jun 12, 2024
Just out, from @Jingyang_zhou and @lyndoryndo at @FlatironCCN and @NYU_CNS Co-existence of Weber’s Law, Steven’s Law, Fechner’s Law (and their generalizations). DOI: 10.1073/pnas.2312293121
Jingyang Zhou
@Jingyang_zhou
Jun 10, 2024
Replying to @Jingyang_zhou
@FlatironInst @FlatironCCN
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Eero Simoncelli
@EeroSimoncelli
Dec 7, 2023
Congratulations to our @FlatironCCN members who will be presenting work at @NeurIPSconf next week - Looking forward to {sharing, discussing, learning} in New Orleans!
7.2K
Eero Simoncelli
@EeroSimoncelli
Aug 27, 2025
Determining optimal partial linear measurements for a signal class is a foundational problem in signal processing. For Gaussian-distributed signals, the solution is PCA. We compute (empirically) a solution for the signal density learned by a denoiser (diffusion model):
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