Zobrazeno 1 - 10
of 9 918
pro vyhledávání: '"Rafiei, A"'
Autor:
Nahid, Md Mahadi Hasan, Rafiei, Davood
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in parsing textual data and generating code. However, their performance in tasks involving tabular data, especially those requiring symbolic reasoning, faces chal
Externí odkaz:
http://arxiv.org/abs/2406.17961
Autor:
Rafiei, Seide Saba, Chevalier, Samuel
DC Optimal Power Flow (DCOPF) is a key operational tool for power system operators, and it is embedded as a subproblem in many challenging optimization problems (e.g., line switching). However, traditional CPU-based solve routines (e.g., simplex) hav
Externí odkaz:
http://arxiv.org/abs/2406.13191
Autor:
Nimmo, Kenzie, Pleunis, Ziggy, Beniamini, Paz, Kumar, Pawan, Lanman, Adam E., Li, D. Z., Main, Robert, Sammons, Mawson W., Andrew, Shion, Bhardwaj, Mohit, Chatterjee, Shami, Curtin, Alice P., Fonseca, Emmanuel, Gaensler, B. M., Joseph, Ronniy C., Kader, Zarif, Kaspi, Victoria M., Lazda, Mattias, Leung, Calvin, Masui, Kiyoshi W., Mckinven, Ryan, Michilli, Daniele, Pandhi, Ayush, Pearlman, Aaron B., Rafiei-Ravandi, Masoud, Sand, Ketan R., Shin, Kaitlyn, Smith, Kendrick, Stairs, Ingrid H.
Fast radio bursts (FRBs) are micro-to-millisecond duration radio transients that originate mostly from extragalactic distances. The emission mechanism responsible for these high luminosity, short duration transients remains debated. The models are br
Externí odkaz:
http://arxiv.org/abs/2406.11053
Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a data-constrained setting. In current meta-learning methods, task learners locally learn models from sensitive data, termed support sets. These task learner
Externí odkaz:
http://arxiv.org/abs/2406.00249
Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only identifies the variability over time, we focus on other dimensions tha
Externí odkaz:
http://arxiv.org/abs/2406.04347
Publikováno v:
IEEE Transactions on Dependable and Secure Computing (2024), pp. 1-17
Machine learning models are vulnerable to maliciously crafted Adversarial Examples (AEs). Training a machine learning model with AEs improves its robustness and stability against adversarial attacks. It is essential to develop models that produce hig
Externí odkaz:
http://arxiv.org/abs/2403.11833
Autor:
Nahid, Md Mahadi Hasan, Rafiei, Davood
Table reasoning is a challenging task that requires understanding both natural language questions and structured tabular data. Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation, but they
Externí odkaz:
http://arxiv.org/abs/2404.10150
Detecting structural similarity between queries is essential for selecting examples in in-context learning models. However, assessing structural similarity based solely on the natural language expressions of queries, without considering SQL queries,
Externí odkaz:
http://arxiv.org/abs/2403.16204
Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often ex
Externí odkaz:
http://arxiv.org/abs/2403.11082
Autor:
Berti, Alessandro, Koren, Istvan, Adams, Jan Niklas, Park, Gyunam, Knopp, Benedikt, Graves, Nina, Rafiei, Majid, Liß, Lukas, Unterberg, Leah Tacke Genannt, Zhang, Yisong, Schwanen, Christopher, Pegoraro, Marco, van der Aalst, Wil M. P.
Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for
Externí odkaz:
http://arxiv.org/abs/2403.01975