The relevant paper: "A Theory of Generalization in Deep Learning". https://arxiv.org/abs/2605.01172
A Theory of Deep Learning
11–20 of 68 posts
Re: A Theory of Deep Learning
#12[deleted]
Re: A Theory of Deep Learning
#13A very fascinating read. As a fellow tufte css enjoyer, Why is user select turned off on the sidenotes? I would like to be able to copy paste them quite badly.
Layout is fine but font is atrocious.
Uppercase letters have different stroke width than lowercase ones — it’s like they are *B*old *L*ike this.
Not only that: tracking, kerning is basically non-existent.
Please don’t use that open-source font
You need real Bembo, not that piece of shit
Re: A Theory of Deep Learning
#14Does anyone happen to know what font this site is using? It looks really elegant.
Font is atrocious.
Uppercase letters have different stroke width than lowercase ones — it’s like they are *B*old *L*ike this.
Not only that: tracking, kerning is basically non-existent.
Please don’t use that open-source font
You need real paid Bembo, not that piece of shit.
Re: A Theory of Deep Learning
#15Does anyone happen to know what font this site is using? It looks really elegant.
apparently its the font used in Edward Tufte's books. Its on github: https://edwardtufte.github.io/et-book/
The "Quantitative Display of Information", which I just checked, is using Monotype Bembo. So still Bembo, but a different version.
Re: A Theory of Deep Learning
#16Idk to me this is just redescribing what deep neural networks do without actually explaining why anything happens. I guess it "unifies" things but I am kinda over most unifying theories. Everything is Bayesian, everything is a graph or a group or some other fancy geometric structure, everything is a category. Ultimately the best framework is whatever is useful enough to explain what's happening in such a way that a practitioner can manipulate the model towards a desired outcome. In other words, where is the knob? The tool they share may be interesting and I hope to play with it to see what happens at different levels of noise applied to the labels.
Re: A Theory of Deep Learning
#17This is a beautifully written way of saying “Some parts of what the network memorizes affect test behavior, and some don’t.” But that’s not a theory of deep learning, the grand unified theory would explain that. We're given a signal channel and a reservoir . Signal lives in the channel, noise lives in the reservoir, and the reservoir supposedly doesn’t show up at test time. Okay, but then we have: why would SGD put t…
[deleted]
Re: A Theory of Deep Learning
#18This is a beautifully written way of saying “Some parts of what the network memorizes affect test behavior, and some don’t.” But that’s not a theory of deep learning, the grand unified theory would explain that. We're given a signal channel and a reservoir . Signal lives in the channel, noise lives in the reservoir, and the reservoir supposedly doesn’t show up at test time. Okay, but then we have: why would SGD put t…
These are the same complaints I had. Also felt like it was high quality ai writing, possibly because of the style choices like "Benign overfitting is noise sitting in the reservoir at interpolation. XYZ is ..." and because of the similarity it has to the times I ended up with chatgpt or gemini creating very detailed and plausible reports about my own crackpot or vague-enough-to-be-useless ideas.
Re: A Theory of Deep Learning
#19This is a beautifully written way of saying “Some parts of what the network memorizes affect test behavior, and some don’t.” But that’s not a theory of deep learning, the grand unified theory would explain that. We're given a signal channel and a reservoir . Signal lives in the channel, noise lives in the reservoir, and the reservoir supposedly doesn’t show up at test time. Okay, but then we have: why would SGD put t…
If that's the case, a way to test the theory and understanding (assuming some parts of reservoir and signal channel can be reliably identified) would be to prune the high-confidence reservoir significantly reducing the model size while still getting good predictions. I don't believe the authors mention this (though I skimmed and didn't read the full paper in detail so I may be wrong)
Re: A Theory of Deep Learning
#20This is a beautifully written way of saying “Some parts of what the network memorizes affect test behavior, and some don’t.” But that’s not a theory of deep learning, the grand unified theory would explain that. We're given a signal channel and a reservoir . Signal lives in the channel, noise lives in the reservoir, and the reservoir supposedly doesn’t show up at test time. Okay, but then we have: why would SGD put t…
> The Borges/Lavoisier stuff is a tell.
Nah, the softer stuff seems like valuable outreach / good science communication for people that aren't up for the math. Including probably lots of software engineers who are sick of dumb debates in forums, and starting to dip into the real literature and listen to better authorities. More people should do this really, since it's the only way to see past the marketing and hype from fully entrenched AI boosters or detractors. Neither of those groups is big on critical thinking, and they dominate most conversation.
Time/effort coming from experts who want to make things accessible is a gift! The paper is linked elsewhere in the thread if you want no-frills.