Zobrazeno 1 - 10
of 1 957
pro vyhledávání: '"Arabshahi, A."'
Autor:
Fotouhi, Milad, Bahadori, Mohammad Taha, Feyisetan, Oluwaseyi, Arabshahi, Payman, Heckerman, David
We investigate the use of in-context learning and prompt engineering to estimate the contributions of training data in the outputs of instruction-tuned large language models (LLMs). We propose two novel approaches: (1) a similarity-based approach tha
Externí odkaz:
http://arxiv.org/abs/2408.11852
Severe weather events such as floods, hurricanes, earthquakes, and large wind or ice storms can cause extensive damage to electrical distribution networks, requiring a multi-day restoration effort. Complicating the recovery process is the lack of com
Externí odkaz:
http://arxiv.org/abs/2404.03197
Autor:
Wysoczanski, Artur, Ettehadi, Nabil, Arabshahi, Soroush, Sun, Yifei, Stukovsky, Karen Hinkley, Watson, Karol E., Han, MeiLan K., Michos, Erin D, Comellas, Alejandro P., Hoffman, Eric A., Laine, Andrew F., Barr, R. Graham, Angelini, Elsa D.
Pulmonary emphysema, the progressive, irreversible loss of lung tissue, is conventionally categorized into three subtypes identifiable on pathology and on lung computed tomography (CT) images. Recent work has led to the unsupervised learning of ten s
Externí odkaz:
http://arxiv.org/abs/2403.00257
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:11360-11397, 2023
ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models are inherently explainable. Yet, interpretable models require extensiv
Externí odkaz:
http://arxiv.org/abs/2307.05350
We use concept-based interpretable models to mitigate shortcut learning. Existing methods lack interpretability. Beginning with a Blackbox, we iteratively carve out a mixture of interpretable experts (MoIE) and a residual network. Each expert explain
Externí odkaz:
http://arxiv.org/abs/2302.10289
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 9, Pp 1919-1934 (2024)
Abstract A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less tech
Externí odkaz:
https://doaj.org/article/7e835cfba9d24c669475afa75e509a7b
Autor:
Tara Khoeini, Ariana Kariminejad, Yalda Nilipour, Armin Ariaei, Hossein Najmabadi, Mojtaba Arabshahi, Mehrshid Faraji Zonooz, Bahram Haghi Ashtiani
Publikováno v:
Clinical Case Reports, Vol 12, Iss 8, Pp n/a-n/a (2024)
Key Clinical Message Homozygous variants of Calcium Voltage‐Gated Channel Subunit Alpha1 S (CACNA1S) gene mutation were previously identified as causes of periodic paralysis and congenital early‐onset myopathy, while it could be manifested as a l
Externí odkaz:
https://doaj.org/article/fbcf03f5597b433ea86d3053625d15bb
Autor:
Singla, Sumedha, Murali, Nihal, Arabshahi, Forough, Triantafyllou, Sofia, Batmanghelich, Kayhan
A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect a high uncertainty on ambiguous in-distribution samples that lie clos
Externí odkaz:
http://arxiv.org/abs/2210.12196
Publikováno v:
Applied Physics Reviews 9, 041403 (2022)
Correlations between electrical and thermal conduction in polymer composites are blurred due to the complex contribution of charge and heat carriers at the nanoscale junctions of filler particles. Conflicting reports on the lack or existence of therm
Externí odkaz:
http://arxiv.org/abs/2209.07635
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract This research involves the development of the spectral collocation method based on orthogonalized Bernoulli polynomials to the solution of time-fractional convection-diffusion problems arising from groundwater pollution. The main aim is to d
Externí odkaz:
https://doaj.org/article/3e6bc5b0acc54164a1ea4061ecaf9d2c