Zobrazeno 1 - 10
of 11 315
pro vyhledávání: '"Ghassemi A"'
Autor:
Hao, Yuexing, Holmes, Jason, Waddle, Mark, Yu, Nathan, Vickers, Kirstin, Preston, Heather, Margolin, Drew, Löckenhoff, Corinna E., Vashistha, Aditya, Ghassemi, Marzyeh, Kalantari, Saleh, Liu, Wei
Cancer patients often struggle to transition swiftly to treatment due to limited institutional resources, lack of sophisticated professional guidance, and low health literacy. The emergence of Large Language Models (LLMs) offers new opportunities for
Externí odkaz:
http://arxiv.org/abs/2409.19100
The cattle industry has been a major contributor to the economy of many countries, including the US and Canada. The integration of Artificial Intelligence (AI) has revolutionized this sector, mirroring its transformative impact across all industries
Externí odkaz:
http://arxiv.org/abs/2408.06336
Autor:
Khanmohammadi, Reza, Ghanem, Ahmed I., Verdecchia, Kyle, Hall, Ryan, Elshaikh, Mohamed, Movsas, Benjamin, Bagher-Ebadian, Hassan, Luo, Bing, Chetty, Indrin J., Alhanai, Tuka, Thind, Kundan, Ghassemi, Mohammad M.
Large Language Models (LLMs) offer significant potential for clinical symptom extraction, but their deployment in healthcare settings is constrained by privacy concerns, computational limitations, and operational costs. This study investigates the op
Externí odkaz:
http://arxiv.org/abs/2408.04775
Spin Hamiltonians, like the Heisenberg model, are used to describe magnetic properties of exchange-coupled molecules and solids. For finite clusters, physical quantities such as heat capacities, magnetic susceptibilities or neutron-scattering spectra
Externí odkaz:
http://arxiv.org/abs/2408.04601
Autor:
Ghassemi, Mohammad M., Alhanai, Tuka
We investigated how participation in machine-arranged meetings were associated with feelings of institutional belonging and perceptions of demographic groups. We collected data from 535 individuals who participated in a program to meet new friends. D
Externí odkaz:
http://arxiv.org/abs/2407.19565
Autor:
Selvi, Aras, Kreacic, Eleonora, Ghassemi, Mohsen, Potluru, Vamsi, Balch, Tucker, Veloso, Manuela
Empirical risk minimization often fails to provide robustness against adversarial attacks in test data, causing poor out-of-sample performance. Adversarially robust optimization (ARO) has thus emerged as the de facto standard for obtaining models tha
Externí odkaz:
http://arxiv.org/abs/2407.13625
Autor:
Alhamoud, Kumail, Ghunaim, Yasir, Alfarra, Motasem, Hartvigsen, Thomas, Torr, Philip, Ghanem, Bernard, Bibi, Adel, Ghassemi, Marzyeh
For medical imaging AI models to be clinically impactful, they must generalize. However, this goal is hindered by (i) diverse types of distribution shifts, such as temporal, demographic, and label shifts, and (ii) limited diversity in datasets that a
Externí odkaz:
http://arxiv.org/abs/2407.08822
Autor:
Zhang, Haoran, Balagopalan, Aparna, Oufattole, Nassim, Jeong, Hyewon, Wu, Yan, Zhu, Jiacheng, Ghassemi, Marzyeh
Large repositories of image-caption pairs are essential for the development of vision-language models. However, these datasets are often extracted from noisy data scraped from the web, and contain many mislabeled examples. In order to improve the rel
Externí odkaz:
http://arxiv.org/abs/2407.18941
Autor:
Jain, Saachi, Hamidieh, Kimia, Georgiev, Kristian, Ilyas, Andrew, Ghassemi, Marzyeh, Madry, Aleksander
Machine learning models can fail on subgroups that are underrepresented during training. While techniques such as dataset balancing can improve performance on underperforming groups, they require access to training group annotations and can end up re
Externí odkaz:
http://arxiv.org/abs/2406.16846
Estimating the uncertainty of a model's prediction on a test point is a crucial part of ensuring reliability and calibration under distribution shifts. A minimum description length approach to this problem uses the predictive normalized maximum likel
Externí odkaz:
http://arxiv.org/abs/2406.02745