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Autor:
Ana Mayorgas Rodríguez
Publikováno v:
Gerión, Vol 41, Iss 1 (2023)
Externí odkaz:
https://doaj.org/article/208f13d375ff48bd8bdce061aa683d2e
Background. The rapid growth of Language Models (LMs), particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impact. Optimizing LMs inference for energy efficiency i
Externí odkaz:
http://arxiv.org/abs/2412.15441
Autor:
de Martino, Vincenzo, Castaño, Joel, Palomba, Fabio, Franch, Xavier, Martínez-Fernández, Silverio
Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Objectives: Our objective is to establish a practical framework for fu
Externí odkaz:
http://arxiv.org/abs/2411.09974
Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. Aims: We apply an SE-oriented c
Externí odkaz:
http://arxiv.org/abs/2411.09683
The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Face (HF) enable the development, sharing, and deployment of these model
Externí odkaz:
http://arxiv.org/abs/2411.09645
As machine learning (ML) and artificial intelligence (AI) technologies become increasingly prevalent in society, concerns about their environmental sustainability have grown. Developing and deploying ML-enabled systems, especially during training and
Externí odkaz:
http://arxiv.org/abs/2410.06708
Background: Given the fast-paced nature of today's technology, which has surpassed human performance in tasks like image classification, visual reasoning, and English understanding, assessing the impact of Machine Learning (ML) on energy consumption
Externí odkaz:
http://arxiv.org/abs/2409.12878
A synthetic Mirnov coils diagnostic is presented and used to study the capabilities of the poloidal array of single-axis coils and the two helical arrays of tri-axial coils installed in the TJ-II stellarator. This tool integrates the plasma currents
Externí odkaz:
http://arxiv.org/abs/2409.04221
Autor:
Omar, Rafiullah, Bogner, Justus, Muccini, Henry, Lago, Patricia, Martínez-Fernández, Silverio, Franch, Xavier
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ensemble learning, i.e., forwarding the same request to several models and
Externí odkaz:
http://arxiv.org/abs/2407.02914