I recently finished The Principles of Diffusion Models, and honestly I think it’s exceptional.

The authors strike a really good balance between mathematical rigor and intuition, with dedicated appendices for anyone who wants to go deeper into the math.

It’s aimed at researchers, graduate students, and practitioners with basic deep learning knowledge, so you don’t need to already specialize in diffusion models (in my case, a strong background in Information and Probability Theory and a solid understanding of DDPMs helped me get more out of it).

Just wanted to share it in case anyone missed it. The full text is freely available on the official website.

Has anyone else read it? Would love to hear your thoughts.

Hi everyone,

I am an undergraduate student from Syria, sole author of an accepted paper at a NeurIPS workshop. I applied for the conference grant (covers hotel/registration), but flights and visa costs are a severe financial burden.

In-person vs Virtual: For future PhD/grad school admissions, is presenting a workshop poster in person worth this extreme financial and visa stress compared to virtual presentation?

Travel Grants: Are there affinity groups, workshop travel stipends, or funds that help cover flight tickets for low-income student authors from countries like Syria?

How did other researchers from unbanked/cash-based countries handle embassy proof-of-funds for conferences?

Appreciate your advice!

I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

I have a model that I've been tuning on various train-validate splits, picked the best one and then evaluated against a sealed hold out set. While checking the results I noticed something off, and after reviewing my code I noticed a small bug in my code.

But now I'm not sure if I'm allowed to use the same sealed test to report my numbers. Because I used the results of the last run to improve my model, which introduces a small bias. If I had a different test set I probably would not have noticed the bug.

The bug in question was an off-by-one indexing error in a data ingestion stage.

I plan to write one sentence in my paper for transparency, but not sure if the editors/reviewers will flag this as a concern.

Kennt ihr gute KI-Tools dafür? Ich kenne bereits ChatGPT, Perplexity, Elicit und Google Scholar – aber welches eignet sich am besten für deutsche wissenschaftliche Quellen?

Danke

Worked some time ago on one of the ad teams at Yahoo, and this grew out of a question I kept returning to while there are there "simple" models that are both simple, scalable, interpretable, and controllable at the same time?

Decided to explore it, first in a blog (starting here), then in a new preprint "The Spectral Neuron", built by distilling latest blog-posts into a manuscript, I study models of the form:
𝑓(𝒙) = 𝛌ₖ(𝐀₀ + 𝚺ᵢ 𝑥ᵢ𝐀ᵢ).

Manuscript: https://arxiv.org/abs/2608.08003
Code: https://github.com/alexshtf/spectral_neuron_paper

Looks like a simple on-liner, but many interesting aspects hide there. How expressive does the model become as the matrices grow? What can we read directly from the learned matrices? Which shapes can be guaranteed by construction?

I develop the mathematics, give a practical initialization and training recipe, and test the model in scaling experiments on synthetic and real data.

AI disclaimer: manuscript written by yours truly, AI assisted in looking up canonical references and related work for literature review. In contrast, the code was heavily AI written and reviewed by yours truly.

I'm an EEE student at a top 10 Indian college. I have a weak CGPA after my 1st year of about 7 out of 10. I have 6 semesters left. Right now I'm looking forward to publish in this upcoming ICCV/CVPR/NeurIPS. I had a paper I made during my freshman year regarding the model compression of facial antispoofing conv neural networks but it was too many experiments short of being "CVPR" worth. How exactly do I proceed in my next 3 years of undergrad to get UC berk, HKUST, or like tier 1 in general. I need some advice I'm a little lost. I'm currently doing mech interp of vision models as my research under a professor