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Stephanie Chan

@scychan.bsky.social
1K followers 277 following 23 posts

Staff Research Scientist at Google DeepMind. Artificial and biological brains 🤖 🧠

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Stephanie Chan @scychan.bsky.social · 21/09/2026
What does the *empirical evidence* tell us about work and wellbeing? And what does that imply for AI futures? arxiv.org/abs/2609.11019 We reviewed studies of populations across the world, including the unemployed, retirees, lottery winners, and financially dependent spouses. Major takeaways:
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Reposted by Stephanie Chan
Andrew Lampinen @lampinen.bsky.social · 05/08/2025
In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:
What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns
Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices
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Stephanie Chan @scychan.bsky.social · 06/06/2025
Great new paper by @jessegeerts.bsky.social, looking at a certain type of generalization in transformers -- transitive inference -- and what conditions induce this type of generalization
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Stephanie Chan @scychan.bsky.social · 02/05/2025
New paper: Generalization from context often outperforms generalization from finetuning. And you might get the best of both worlds by spending extra compute and train time to augment finetuning.
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Stephanie Chan @scychan.bsky.social · 11/03/2025
New work led by @aaditya6284.bsky.social "Strategy coopetition explains the emergence and transience of in-context learning in transformers." We find some surprising things!! E.g. that circuits can simultaneously compete AND cooperate ("coopetition") 😯 🧵👇
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Stephanie Chan @scychan.bsky.social · 04/01/2025
Sadly, we have lost a brilliant researcher and colleague, Felix Hill. Please see this note, where I have tried to compile some of his writings: docs.google.com/document/d/1...
docs.google.com
For Felix
Devastatingly, we have lost a bright light in our field. Felix Hill was not only a deeply insightful thinker -- he was also a generous, thoughtful mentor to many researchers. He majorly changed my lif...
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Reposted by Stephanie Chan
Andrew Lampinen @lampinen.bsky.social · 10/12/2024
What counts as in-context learning (ICL)? Typically, you might think of it as learning a task from a few examples. However, we’ve just written a perspective (arxiv.org/abs/2412.03782) suggesting interpreting a much broader spectrum of behaviors as ICL! Quick summary thread: 1/7
arxiv.org
The broader spectrum of in-context learning
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning...
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Reposted by Stephanie Chan
Noémi Éltető @noemielteto.bsky.social · 09/12/2024
Introducing the :milkfoamo: emoji
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Stephanie Chan @scychan.bsky.social · 09/12/2024
I'll be not at Neurips this week. Let's grab coffee if you want to fomo-commiserate with me
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Stephanie Chan @scychan.bsky.social · 09/12/2024
Hello hello. Testing testing 123
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