postdoc @UCB_MillerInst PhD @ICMEStanford creator @SeeingTheory
UC Berkeley
Joined December 2020
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For the last few years, a lot of my work has been driven by the feeling that deep learning is not magic — there are principles, mechanisms, and laws waiting to be understood.
This paper is our attempt to say that clearly!
1/ Deep learning is going to have a scientific theory. We can see the pieces starting to come together, and it's looking a lot like physics!
We're releasing a paper pulling together these emerging threads and giving them a name: learning mechanics.
🔨 arxiv.org/pdf/2604.21691 🔧
Had a great time visiting @TTIC_Connect last year -- the ideas I presented have progressed quite a bit since then:
- arxiv.org/pdf/2506.06489
- arxiv.org/pdf/2602.03655
1/4 The Dynamics of Deep Learning. "Trying to understand deep learning...comes down to understanding this interaction of three components that make the deep learning system," said @KuninDaniel, speaking at TTIC in April last year. Video: youtube.com/watch?v=4eMW8Hir…
The Miller Fellowship has been an awesome opportunity and Berkeley is a pretty great place to be!
Scientist @KuninDaniel (MF 25-28) studies learning in neural networks to uncover principles of intelligence in artificial & biological systems. The Miller Fellowship gives me freedom to take risks and grow as a researcher & communicator. Nominate by 9/10: bit.ly/millerfellowship
Daniel Kunin retweeted
If you’re a researcher in math or CS wondering what will be left to work on as AI charges through our fields, rest assured: the final theory is still out there waiting for us. It’s not even fully formulated let alone solved. What is the structure and form of thought itself?
A good view at emerging theory on deep learning
arxiv.org/abs/2604.21691
New work by @_AmilDravid, extending his really neat work on Rosetta Neurons through the lens of scaling laws!
A nice example of how mech. interp and learning mechanics can complement one another, bringing different perspectives that together lead to deeper insights
Scaling laws describe how loss changes with scale. Do neurons inside models change predictably too?
We study vision and language models up to 30B params and find systematic scaling in neuron universality, specialization, and selectivity.
Paper+code: avdravid.github.io/rosetta-n…
1/n
Daniel Kunin retweeted
A longstanding dream of interp is to decompose activations into distinct, interpretable parts.
But when should we expect that to work, and what even are such parts?
New from Simplex: transformers factor their world into orthogonal subspaces, even when it costs accuracy.🧵👇
Excited to share that our paper “Sequential Group Composition: A Window into the Mechanics of Deep Learning” was accepted to ICML 2026 in Seoul!
Co-led with @giovannimarchet
and @AdeleMyersPhD @hopfbifurcator @ninamiolane
Paper: arxiv.org/abs/2602.03655
Yes — by leveraging associativity.
We explicitly construct efficient solutions: RNNs can compose elements sequentially in k steps, while deep MLPs can compose adjacent pairs in parallel in log k layers
and we find preliminary evidence that GD can discover these solutions!
For me, this paper is learning mechanics in action!
Mech interp first identified that NNs use Fourier features in algebraic tasks - great work @bilalchughtai_ @justanotherlaw @NeelNanda5
Learn mech asks why training produced those features, in that order, with that architecture
Daniel Kunin retweeted
From "Mathematical theory of deep learning: Can we do it? Should we do it?" to "There Will Be a Scientific Theory of Deep Learning".
It's respectively the title of a talk I gave four years ago, and the title of an arxiv paper from four days ago.
I really like the "learning mechanics" perspective (think of it as a continuation of "statistical mechanics", "quantum mechanics", and so on). Several of my last academic papers can be viewed under that lens (e.g. Learning threshold neurons via the “edge of stability”; or LEGO). I'm not as optimistic as the authors of the recent arxiv paper that we will EVER be able to reach what the "physics mechanics" field have achieved, but it's certainly worth trying.
Talk: youtu.be/3uRD_lg701k?si=yjLY…
Paper: arxiv.org/abs/2604.21691
Daniel Kunin retweeted
Wir mussen wissen. Wir werden wissen
There Will Be a Scientific Theory of Deep Learning
Jamie Simon, Daniel Kunin, Alexander Atanasov, Enric Boix-Adserà, Blake Bordelon, Jeremy Cohen, Nikhil Ghosh, Florentin Guth, Arthur Jacot, Mason Kamb, Dhruva Karkada, …
arxiv.org/abs/2604.21691 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]
ALT In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull together major strands of ongoing research in deep learning theory and identify five growing bodies of work that point toward such a theory: (a) solvable idealized settings that provide intuition for learning dynamics in realistic systems; (b) tractable limits that reveal insights into fundamental learning phenomena; (c) simple mathematical laws that capture important macroscopic observables; (d) theories of hyperparameters that disentangle them from the rest of the training process, leaving simpler systems behind; and (e) universal behaviors shared across systems and settings which clarify which phenomena call for explanation. Taken together, these bodies of work share certain broad traits: they are concerned with
Daniel Kunin retweeted
Very happy to be part of this project. We've compiled the main reasons why a Theory of Deep Learning is possible if not inevitable!
1/ Deep learning is going to have a scientific theory. We can see the pieces starting to come together, and it's looking a lot like physics!
We're releasing a paper pulling together these emerging threads and giving them a name: learning mechanics.
🔨 arxiv.org/pdf/2604.21691 🔧
Daniel Kunin retweeted
Great perspective on the theory of deep learning from a stellar group of authors!Physics-inspired ideas will play a central role in shaping this field. Congrats to my group alumni @blake__bordelon and @ABAtanasov for their contributions here and across many influential papers.
1/ Deep learning is going to have a scientific theory. We can see the pieces starting to come together, and it's looking a lot like physics!
We're releasing a paper pulling together these emerging threads and giving them a name: learning mechanics.
🔨 arxiv.org/pdf/2604.21691 🔧
100% agree. Neuroscience embraces studying the brain at multiple levels — computational, algorithmic, and implementational. I’m excited to see deep learning moving toward the same conversation, with theory and interpretability informing each other!
Daniel Kunin retweeted
Great to see the next generation taking the lead in the science of deep learning! Also proud that two brilliant members/alumni of my group are a part of this: @KuninDaniel & @MasonKamb
1/ Deep learning is going to have a scientific theory. We can see the pieces starting to come together, and it's looking a lot like physics!
We're releasing a paper pulling together these emerging threads and giving them a name: learning mechanics.
🔨 arxiv.org/pdf/2604.21691 🔧
Daniel Kunin retweeted
There Will Be a Scientific Theory of Deep Learning ift.tt/FIXLaes