https://botfrens.com/collections/46/contents/15543
https://botfrens.com/collections/46/contents/15543
How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔
Announcing our #NeurIPS2025 📄 that dives into this.
🧵below
#AIResearch #MachineLearning #LLM
How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔
Announcing our #NeurIPS2025 📄 that dives into this.
🧵below
#AIResearch #MachineLearning #LLM
🧵⬇️ Our @alleninstitute.org #NeurIPS2025 paper shows heavy-tailed weights can strongly affect dynamics, trade off robustness + attractor dimension.
🧵⬇️ Our @alleninstitute.org #NeurIPS2025 paper shows heavy-tailed weights can strongly affect dynamics, trade off robustness + attractor dimension.
One more on estimating dynamics of complex systems, this time with symbolic regression
doi.org/10.1038/s435...
One more on estimating dynamics of complex systems, this time with symbolic regression
doi.org/10.1038/s435...
I argued that our overly specialized, modular approach to studying the brain has given us a simplistic view of readout.
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#neuroskyence
www.thetransmitter.org/the-big-pict...
I argued that our overly specialized, modular approach to studying the brain has given us a simplistic view of readout.
🧠📈
In this new preprint, we found that the hippocampus sequentially inverts the behaviorally relevant dimensions of similarity 🧵
www.biorxiv.org/content/10.1...
In this new preprint, we found that the hippocampus sequentially inverts the behaviorally relevant dimensions of similarity 🧵
www.biorxiv.org/content/10.1...
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
Can we simultaneously learn transformation-invariant and transformation-equivariant representations with self-supervised learning?
TL;DR Yes! This is possible via simple predictive learning & architectural inductive biases – without extra loss terms and predictors!
🧵 (1/10)
Charlotte has developed a theory for how learning curriculum influences learning generalization.
Our theory makes straightforward neural predictions that can be tested in future experiments. (1/4)
🧠🤖 🧠📈 #MLSky
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We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up.
🧠📈
A 🧵:
tinyurl.com/yr8tawj3
Charlotte has developed a theory for how learning curriculum influences learning generalization.
Our theory makes straightforward neural predictions that can be tested in future experiments. (1/4)
🧠🤖 🧠📈 #MLSky
by René Magritte
by René Magritte
www.nature.com/articles/s41...
www.nature.com/articles/s41...