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pro vyhledávání: '"Refael, Yehonathan"'
Large Language Models (LLMs) have shown promise in highly-specialized domains, however challenges are still present in aspects of accuracy and costs. These limitations restrict the usage of existing models in domain-specific tasks. While fine-tuning
Externí odkaz:
http://arxiv.org/abs/2410.21479
Training and fine-tuning large language models (LLMs) come with challenges related to memory and computational requirements due to the increasing size of the model weights and the optimizer states. Various techniques have been developed to tackle the
Externí odkaz:
http://arxiv.org/abs/2410.17881
Autor:
Refael, Yehonathan, Hakim, Adam, Greenberg, Lev, Aviv, Tal, Lokam, Satya, Fishman, Ben, Seidman, Shachar
Large language models (LLMs) have recently seen widespread adoption, in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting enormous investments by their owners. Moreover, the high cost of clo
Externí odkaz:
http://arxiv.org/abs/2407.10886
Publikováno v:
Transactions on Machine Learning Research, 2024, ISSN 2835-8856
The extensive need for computational resources poses a significant obstacle to deploying large-scale Deep Neural Networks (DNN) on devices with constrained resources. At the same time, studies have demonstrated that a significant number of these DNN
Externí odkaz:
http://arxiv.org/abs/2212.12921
Autor:
Huleihel, Wasim, Refael, Yehonathan
Social media platforms (SMPs) leverage algorithmic filtering (AF) as a means of selecting the content that constitutes a user's feed with the aim of maximizing their rewards. Selectively choosing the contents to be shown on the user's feed may yield
Externí odkaz:
http://arxiv.org/abs/2209.05550
Akademický článek
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We propose an efficient method to learn both unstructured and structured sparse neural networks during training, using a novel generalization of the sparse envelope function (SEF) used as a regularizer, termed {\itshape{group sparse envelope function
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::147ee7f630275e73a09da7a8dc51372a
http://arxiv.org/abs/2212.12921
http://arxiv.org/abs/2212.12921
Autor:
Huleihel, Wasim, Refael, Yehonathan
Social media platforms (SMPs) leverage algorithmic filtering (AF) as a means of selecting the content that constitutes a user's feed with the aim of maximizing their rewards. Selectively choosing the contents to be shown on the user's feed may yield
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a40985a6347377ba12c2160c12913854
http://arxiv.org/abs/2209.05550
http://arxiv.org/abs/2209.05550
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
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Autor:
Beck, Amir, Refael, Yehonathan
Publikováno v:
Journal of Global Optimization; Mar2022, Vol. 82 Issue 3, p463-482, 20p
