Zobrazeno 1 - 10
of 11 594
pro vyhledávání: '"A. A. Mehrabi"'
Agricultural workers play a vital role in the global economy and food security by cultivating, transporting, and processing food for populations worldwide. Despite their importance, detailed spatial data on the global agricultural workforce have rema
Externí odkaz:
http://arxiv.org/abs/2412.15841
Autor:
Pierson, Matthew, Mehrabi, Zia
Waterways shape earth system processes and human societies, and a better understanding of their distribution can assist in a range of applications from earth system modeling to human development and disaster response. Most efforts to date to map the
Externí odkaz:
http://arxiv.org/abs/2412.00050
Autor:
Pierson, Matthew, Mehrabi, Zia
Surprisingly a number of Earth's waterways remain unmapped, with a significant number in low and middle income countries. Here we build a computer vision model (WaterNet) to learn the location of waterways in the United States, based on high resoluti
Externí odkaz:
http://arxiv.org/abs/2411.13590
A quantum analogue of the Central Limit Theorem (CLT), first introduced by Cushen and Hudson (1971), states that the $n$-fold convolution $\rho^{\boxplus n}$ of an $m$-mode quantum state $\rho$ with zero first moments and finite second moments conver
Externí odkaz:
http://arxiv.org/abs/2410.21998
High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning ena
Externí odkaz:
http://arxiv.org/abs/2411.03322
Autor:
Meng, Tao, Mehrabi, Ninareh, Goyal, Palash, Ramakrishna, Anil, Galstyan, Aram, Zemel, Richard, Chang, Kai-Wei, Gupta, Rahul, Peris, Charith
We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. Given a training corpus and control criteria formulated as a sequence-level constraint on model outputs, our method fine-tunes the LLM on the
Externí odkaz:
http://arxiv.org/abs/2410.05559
Data is a crucial element in large language model (LLM) alignment. Recent studies have explored using LLMs for efficient data collection. However, LLM-generated data often suffers from quality issues, with underrepresented or absent aspects and low-q
Externí odkaz:
http://arxiv.org/abs/2410.05269
Autor:
Markowitz, Elan, Ramakrishna, Anil, Dhamala, Jwala, Mehrabi, Ninareh, Peris, Charith, Gupta, Rahul, Chang, Kai-Wei, Galstyan, Aram
Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are often developed separately and must be integrated after training. We intro
Externí odkaz:
http://arxiv.org/abs/2407.21358
Autor:
Ghahremani, Tanaz, Hoseyni, Mohammad, Ahmadi, Mohammad Javad, Mehrabi, Pouria, Nikoofard, Amirhossein
Data augmentation is a key technique for addressing the challenge of limited datasets, which have become a major component in the training procedures of image processing. Techniques such as geometric transformations and color space adjustments have b
Externí odkaz:
http://arxiv.org/abs/2405.04442
Autor:
Le, Vy, Nissen, Jayson M., Tang, Xiuxiu, Zhang, Yuxiao, Mehrabi, Amirreza, Morphew, Jason W., Chang, Hua Hua, Van Dusen, Ben
In physics education research, instructors and researchers often use research-based assessments (RBAs) to assess students' skills and knowledge. In this paper, we support the development of a mechanics cognitive diagnostic to test and implement effec
Externí odkaz:
http://arxiv.org/abs/2404.00009