Sample-efficient active learning for materials informatics using integrated posterior variance

· · 来源:tutorial资讯

Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.

LLMs used tactical nuclear weapons in 95% of AI war games, launched strategic strikes three times

Buy Pokémo。关于这个话题,下载安装汽水音乐提供了深入分析

«Гражданская война, которая была у нас после революции, тоже наложила свои отпечатки, разделение, а после этого, мы помним, что много офицеров, уехавших из России во время Великой Отечественной войны, защищали Советский Союз», — привел пример Чепа.,这一点在WPS官方版本下载中也有详细论述

APPSO 在昨天的文章里也有提到:Anthropic「蒸馏」了人类最大的知识库

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