Senior Research Scientist @ DeepMind. I work on LLM post-training, focusing on improving training data. Prior to that, I worked on AlphaFold. Typos are my own.

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  A year ago I asked: Is there more than Self-Attention and Deep Sets? - and got very insightful answers. 🙏 Now, Ed, Martin and I wrote up our own take on the various neural networks architectures for sets. Have a look and tell us what you think! :) ➡️fabianfuchsml.github.io/learningonsets/ ☕️

  

  

  Both Max-Pooling (e.g. DeepSets) and Self-Attention are permutation invariant/equivariant neural network architectures for set-based problems. I am aware of a couple of variations for both of these. Are there additional, fundamentally different architectures for sets? 🤔

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  I wrote a blog post trying to understand TrackStar, a gradient-based method tracing LLM predictions to influential training examples. Mostly: take the main equation, read it from right to left, and poke at the pieces with a small MNIST toy example. 🙂 ☕️ fabianfuchsml.github.io/trackstar/

  

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  Graphs , Sets, Universality We put more work into this and are presenting it via the ICLR blogpost track (thanks to organisers and reviewers!). Have a read and let us know what you think: iclr-blogposts.github.io/2023/blog/2023… better in light mode💡, dark mode🌙 messes with the latex a bit

  

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  I have recently had a range of very insightful conversations with

  @PetarV_93

  about graph neural networks, networks on sets, universality and how ideas have spread in the two communities. This is our write up, feedback welcome as always! :) ➡️fabianfuchsml.github.io/universalgraphs ☕️

  

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  Graph neural networks often have to globally aggregate over all nodes. How we do this can have a significant impact on performance 🎯. After we recently finished a project on this, I wrote a blog post on this topic. Let me know what you think! :) ➡️fabianfuchsml.github.io/equilibriumagg… ☕️