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halfsatori.bsky.social
@halfsatori.bsky.social
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johannesbrinz.bsky.social
📚 For me, this is the best philosophy book of 2024! 🌟 Highly recommend it to anyone interested in the philosophy of (neuro)science. Plus, it's open access!

Check it out here: direct.mit.edu/books/oa-mon...

#Philosophy #Neuroscience #OpenAccess
December 20, 2024 at 9:39 AM
Reposted
ferrisjabr.bsky.social
I was deeply disappointed by the lack of nature/science/climate/enviro on many major end-of-year book lists—so I decided to make my own!

Introducing: ✨🎁📚 The 2024 Holiday Gift Guide to Nature & Science Books ✨🎁📚

Please share: Let's make this go viral in time for Black Friday / holiday shopping!
A collage of book covers of new nature & science books. Featured here are Atlas Obscura Wild Life, Dinosaurs at the Dinner Party, Every Living Thing, Alien Earths, Becoming Earth, and Deep Water A collage of book covers of new nature & science books. Featured here are Frostbite, The Inner Clock, How to Kill an Asteroid, The Great River, The Last Fire Season, and Hoof Beats A collage of book covers of new nature & science books. Featured here are The Serviceberry, Not the End of the World, Nature's Ghosts, Meet the Neighbors, The Light Eaters, and Our Moon A collage of book covers of new nature & science books. Featured here are Turning to Stone, The Weight of Nature, What If We Get It Right?, Waves in An Impossible Sea, The Tree Collectors, and Why We Remember
November 27, 2024 at 7:08 PM
Reposted
brandonrohrer.com
Adding my love letter to

arxiv.org/pdf/2304.01315

Empirical Design in Reinforcement Learning
by
Andrew Patterson, Samuel Neumann, Martha White, Adam White

JMLR 25 (2024) 1-63
#ReinforcementLearning

These aren’t the heroes we deserve, but they are the heroes we need.
arxiv.org
November 23, 2024 at 1:40 PM
halfsatori.bsky.social
Show HN: Llama 3.2 Interpretability with Sparse Autoencoders - github.com/PaulPauls/ll...

well documented!
GitHub - PaulPauls/llama3_interpretability_sae: A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.
A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible. - PaulPauls/llama3_interpretability_sae
github.com
November 21, 2024 at 9:44 PM