Zobrazeno 1 - 10
of 4 558
pro vyhledávání: '"A Khorshidi"'
Autor:
Mackraz, Natalie, Sivakumar, Nivedha, Khorshidi, Samira, Patel, Krishna, Theobald, Barry-John, Zappella, Luca, Apostoloff, Nicholas
Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuni
Externí odkaz:
http://arxiv.org/abs/2412.03537
This study presents an uncertainty-aware stacked neural networks model for the reliable classification of COVID-19 from radiological images. The model addresses the critical gap in uncertainty-aware modeling by focusing on accurately identifying conf
Externí odkaz:
http://arxiv.org/abs/2410.02805
Accurate inertial parameter identification is crucial for the simulation and control of robots encountering intermittent contact with the environment. Classically, robots' inertial parameters are obtained from CAD models that are not precise (and som
Externí odkaz:
http://arxiv.org/abs/2409.09850
Autor:
Qian, Kun, Sang, Yisi, Bayat, Farima Fatahi, Belyi, Anton, Chu, Xianqi, Govind, Yash, Khorshidi, Samira, Khot, Rahul, Luna, Katherine, Nikfarjam, Azadeh, Qi, Xiaoguang, Wu, Fei, Zhang, Xianhan, Li, Yunyao
Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in specific tasks. Incorporating few-shot examples is a vital and effective
Externí odkaz:
http://arxiv.org/abs/2408.04637
In this paper, we introduce a novel approach to centroidal state estimation, which plays a crucial role in predictive model-based control strategies for dynamic legged locomotion. Our approach uses the Koopman operator theory to transform the robot's
Externí odkaz:
http://arxiv.org/abs/2403.13366
Autor:
Yazdani, Danial, Branke, Juergen, Khorshidi, Mohammad Sadegh, Omidvar, Mohammad Nabi, Li, Xiaodong, Gandomi, Amir H., Yao, Xin
Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiven
Externí odkaz:
http://arxiv.org/abs/2402.15731
Autor:
Khorshidi, Mohammad Sadegh, Yazdanjue, Navid, Gharoun, Hassan, Yazdani, Danial, Nikoo, Mohammad Reza, Chen, Fang, Gandomi, Amir H.
In machine learning, the exponential growth of data and the associated ``curse of dimensionality'' pose significant challenges, particularly with expansive yet sparse datasets. Addressing these challenges, multi-view ensemble learning (MEL) has emerg
Externí odkaz:
http://arxiv.org/abs/2401.06251
Publikováno v:
Saudi Dental Journal, Vol 36, Iss 12, Pp 1582-1587 (2024)
Objective: Forecasting the complexity of extracting mandibular third molars is crucial for selecting appropriate surgical methods and minimizing postoperative complications. This study aims to develop an AI-driven predictive model using CBCT reports,
Externí odkaz:
https://doaj.org/article/65bf57d859b44792bc90dc1ce5761b12
Publikováno v:
Vaccine Research, Vol 3, Iss 3, Pp 68-73 (2016)
Introduction: Pathogenic strains of Proteus mirabilis have important roles in urinary tract infection. Proteus toxic agglutinin (Pta) is amongst the most important virulence factors of P. mirabilis. This protein has a conserved sequence present in al
Externí odkaz:
https://doaj.org/article/9153285a79a344e4b7381ed8b47d6013
Autor:
Qian, Kun, Belyi, Anton, Wu, Fei, Khorshidi, Samira, Nikfarjam, Azadeh, Khot, Rahul, Sang, Yisi, Luna, Katherine, Chu, Xianqi, Choi, Eric, Govind, Yash, Seivwright, Chloe, Sun, Yiwen, Fakhry, Ahmed, Rekatsinas, Theo, Ilyas, Ihab, Qi, Xiaoguang, Li, Yunyao
The quality of a knowledge graph directly impacts the quality of downstream applications (e.g. the number of answerable questions using the graph). One ongoing challenge when building a knowledge graph is to ensure completeness and freshness of the g
Externí odkaz:
http://arxiv.org/abs/2312.09424