Zobrazeno 1 - 10
of 29 924
pro vyhledávání: '"P. Saleem"'
Autor:
The LCM team, Barrault, Loïc, Duquenne, Paul-Ambroise, Elbayad, Maha, Kozhevnikov, Artyom, Alastruey, Belen, Andrews, Pierre, Coria, Mariano, Couairon, Guillaume, Costa-jussà, Marta R., Dale, David, Elsahar, Hady, Heffernan, Kevin, Janeiro, João Maria, Tran, Tuan, Ropers, Christophe, Sánchez, Eduardo, Roman, Robin San, Mourachko, Alexandre, Saleem, Safiyyah, Schwenk, Holger
LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto tool for many tasks. The current established technology of LLMs is to process input and generate output at the token level. This is in sharp contrast to hu
Externí odkaz:
http://arxiv.org/abs/2412.08821
Early detection and resolution of duplicate and conflicting requirements can significantly enhance project efficiency and overall software quality. Researchers have developed various computational predictors by leveraging Artificial Intelligence (AI)
Externí odkaz:
http://arxiv.org/abs/2412.01657
Traditional language models have been extensively evaluated for software engineering domain, however the potential of ChatGPT and Gemini have not been fully explored. To fulfill this gap, the paper in hand presents a comprehensive case study to inves
Externí odkaz:
http://arxiv.org/abs/2412.00959
Understanding future weather changes at regional and local scales is crucial for planning and decision-making, particularly in the context of extreme weather events, as well as for broader applications in agriculture, insurance, and infrastructure de
Externí odkaz:
http://arxiv.org/abs/2411.14774
Autor:
Awad, Ali, Saleem, Ashraf, Paheding, Sidike, Lucas, Evan, Al-Ratrout, Serein, Havens, Timothy C.
Underwater imagery often suffers from severe degradation that results in low visual quality and object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their impact on underwater object detectio
Externí odkaz:
http://arxiv.org/abs/2411.14626
This study investigates the astronomical implications of the Ghosh-Kumar rotating Black Hole (BH), particularly its behaviour on shadow images, illuminated by celestial light sources and equatorial thin accretion disks. Our research delineates a cruc
Externí odkaz:
http://arxiv.org/abs/2411.11807
The work reports nonmagnetic behavior (0.04 $\mu$B) in two-dimensional (2D) V2C-OF MXene and ferromagnetism in MnO$_2$ adsorbed V2C-OF MXene. The density functional theory (DFT) calculations were carried out to study the magnetic moments of V$_2$C-OF
Externí odkaz:
http://arxiv.org/abs/2411.09536
We introduce Visual Premise Proving (VPP), a novel task tailored to refine the process of chart question answering by deconstructing it into a series of logical premises. Each of these premises represents an essential step in comprehending a chart's
Externí odkaz:
http://arxiv.org/abs/2410.22492
Climate models serve as critical tools for evaluating the effects of climate change and projecting future climate scenarios. However, the reliance on numerical simulations of physical equations renders them computationally intensive and inefficient.
Externí odkaz:
http://arxiv.org/abs/2410.21657
Publikováno v:
Neural Information Processing Systems (NeurIPS). Machine Learning with New Compute Paradigms (MLNCP) Workshop, October 2024
We present a novel approach for accelerating AI performance by leveraging Anderson extrapolation, a vector-to-vector mapping technique based on a window of historical iterations. By identifying the crossover point where a mixing penalty is incurred,
Externí odkaz:
http://arxiv.org/abs/2410.19460