Zobrazeno 1 - 10
of 37 595
pro vyhledávání: '"Assaf AT"'
Autor:
Bercovich, Akhiad, Ronen, Tomer, Abramovich, Talor, Ailon, Nir, Assaf, Nave, Dabbah, Mohammad, Galil, Ido, Geifman, Amnon, Geifman, Yonatan, Golan, Izhak, Haber, Netanel, Karpas, Ehud, Levy, Itay, Mor, Shahar, Moshe, Zach, Nabwani, Najeeb, Puny, Omri, Rubin, Ran, Schen, Itamar, Shahaf, Ido, Tropp, Oren, Argov, Omer Ullman, Zilberstein, Ran, El-Yaniv, Ran
Large language models (LLMs) have demonstrated remarkable capabilities, but their adoption is limited by high computational costs during inference. While increasing parameter counts enhances accuracy, it also widens the gap between state-of-the-art c
Externí odkaz:
http://arxiv.org/abs/2411.19146
Autor:
Assaf, Dorit, Adorni, Giorgia, Lutz, Elia, Negrini, Lucio, Piatti, Alberto, Mondada, Francesco, Mangili, Francesca, Gambardella, Luca Maria
This paper addresses the incorporation of problem decomposition skills as an important component of computational thinking (CT) in K-12 computer science (CS) education. Despite the growing integration of CS in schools, there is a lack of consensus on
Externí odkaz:
http://arxiv.org/abs/2411.14945
Autor:
Lahiany, Assaf, Gal, Oren
Curriculum Learning (CL), drawing inspiration from natural learning patterns observed in humans and animals, employs a systematic approach of gradually introducing increasingly complex training data during model development. Our work applies innovati
Externí odkaz:
http://arxiv.org/abs/2411.13438
Autor:
Doveh, Sivan, Shabtay, Nimrod, Lin, Wei, Schwartz, Eli, Kuehne, Hilde, Giryes, Raja, Feris, Rogerio, Karlinsky, Leonid, Glass, James, Arbelle, Assaf, Ullman, Shimon, Mirza, M. Jehanzeb
Vision-Language Models (VLMs) have shown remarkable capabilities across diverse visual tasks, including image recognition, video understanding, and Visual Question Answering (VQA) when explicitly trained for these tasks. Despite these advances, we fi
Externí odkaz:
http://arxiv.org/abs/2411.13317
Autor:
Atias, Eyal, Assaf, Michael
Understanding the dynamics of an epidemic spread is crucial for effective control measures. During the COVID-19 pandemic, quarantines were implemented to minimize infections while mitigating social and economic impacts, raising the question of how to
Externí odkaz:
http://arxiv.org/abs/2411.11368
Humans and other organisms make decisions choosing between different options, with the aim to maximize the reward and minimize the cost. The main theoretical framework for modeling the decision-making process has been based on the highly successful d
Externí odkaz:
http://arxiv.org/abs/2411.11143
Recent research increasingly focuses on training vision-language models (VLMs) with long, detailed image captions. However, small-scale VLMs often struggle to balance the richness of these captions with the risk of hallucinating content during fine-t
Externí odkaz:
http://arxiv.org/abs/2411.09018
We prove that if $(\mathcal{M},d)$ is an $n$-point metric space that embeds quasisymmetrically into a Hilbert space, then for every $\tau>0$ there is a random subset $\mathcal{Z}$ of $\mathcal{M}$ such that for any pair of points $x,y\in \mathcal{M}$
Externí odkaz:
http://arxiv.org/abs/2410.21931
Autor:
Castle, Benjamin, Hasson, Assaf
Generalizing previous work on algebraically closed valued fields (ACVF) and o-minimal fields, we study strongly minimal relics of real closed valued fields (RCVF), and more generally T-convex expansions of o-minimal fields. Our main result (replicati
Externí odkaz:
http://arxiv.org/abs/2410.22442
We introduce CompAct, a technique that reduces peak memory utilization on GPU by 25-30% for pretraining and 50% for fine-tuning of LLMs. Peak device memory is a major limiting factor in training LLMs, with various recent works aiming to reduce model
Externí odkaz:
http://arxiv.org/abs/2410.15352