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Akademický článek
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Autor:
Cristine Gorski Severo, Edair Görski
Publikováno v:
Fórum Linguístico, Vol 20, Iss 4 (2024)
Externí odkaz:
https://doaj.org/article/023acb83afc248eb86f4e4f310d675b3
Motivated by the experimental identification of magnetic compounds consisting of zigzag chains, we analyze the band structure topology of magnons in ferromagnets on a zigzag lattice. We account for the general lattice geometry by including spatially
Externí odkaz:
http://arxiv.org/abs/2411.18515
For noncovalent interactions (NCIs), the CCSD(T) coupled cluster method is widely regarded as the `gold standard'. With localized orbital approximations, benchmarks for ever larger NCI complexes are being published; yet tantalizing evidence from quan
Externí odkaz:
http://arxiv.org/abs/2410.12603
Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair". Real-world data, however, are typically plagued by various measurement biases and other violated assumptio
Externí odkaz:
http://arxiv.org/abs/2410.09600
Autor:
Shkolnik, Moran, Fishman, Maxim, Chmiel, Brian, Ben-Yaacov, Hilla, Banner, Ron, Levy, Kfir Yehuda
Quantization has established itself as the primary approach for decreasing the computational and storage expenses associated with Large Language Models (LLMs) inference. The majority of current research emphasizes quantizing weights and activations t
Externí odkaz:
http://arxiv.org/abs/2410.03185
Structural understanding of complex visual objects is an important unsolved component of artificial intelligence. To study this, we develop a new technique for the recently proposed Break-and-Make problem in LTRON where an agent must learn to build a
Externí odkaz:
http://arxiv.org/abs/2410.01111
Akademický článek
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Segmentation is a crucial step in microscopy image analysis. Numerous approaches have been developed over the past years, ranging from classical segmentation algorithms to advanced deep learning models. While U-Net remains one of the most popular and
Externí odkaz:
http://arxiv.org/abs/2409.16940
We train, for the first time, large language models using FP8 precision on datasets up to 2 trillion tokens -- a 20-fold increase over previous limits. Through these extended training runs, we uncover critical instabilities in FP8 training that were
Externí odkaz:
http://arxiv.org/abs/2409.12517