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pro vyhledávání: '"Ahmed, E. A."'
Publikováno v:
ECAI 2023. IOS Press, 2023. 629-636
Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on heuristics commonly identifie
Externí odkaz:
http://arxiv.org/abs/2412.18316
Traditional code metrics (product and process metrics) have been widely used in defect prediction. However, these metrics have an inherent limitation: they do not reveal system traits that are tied to certain building blocks of a given programming la
Externí odkaz:
http://arxiv.org/abs/2412.02907
AI judge systems are designed to automatically evaluate Foundation Model-powered software (i.e., FMware). Due to the intrinsic dynamic and stochastic nature of FMware, the development of AI judge systems requires a unique engineering life cycle and p
Externí odkaz:
http://arxiv.org/abs/2411.17793
Autor:
Zhang, Haoxiang, Chang, Shi, Leung, Arthur, Thangarajah, Kishanthan, Chen, Boyuan, Lutfiyya, Hanan, Hassan, Ahmed E.
The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex engineering across various domains. A
Externí odkaz:
http://arxiv.org/abs/2411.09580
As foundation models (FMs) play an increasingly prominent role in complex software systems, such as FM-powered agentic software (i.e., Agentware), they introduce significant challenges for developers regarding observability. Unlike traditional softwa
Externí odkaz:
http://arxiv.org/abs/2411.03455
To help MLOps engineers decide which operator to use in which deployment scenario, this study aims to empirically assess the accuracy vs latency trade-off of white-box (training-based) and black-box operators (non-training-based) and their combinatio
Externí odkaz:
http://arxiv.org/abs/2411.00907
Autor:
Sanner, Antoine P., Stieber, Jonathan, Grauhan, Nils F., Kim, Suam, Brockmann, Marc A., Othman, Ahmed E., Mukhopadhyay, Anirban
Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-learning-based solutions are starting to model complex relations between brain structures, but still st
Externí odkaz:
http://arxiv.org/abs/2411.00578
The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware--software systems that integrate FMs as core components. While building demonstration-level FMware is relatively straightforward, transitio
Externí odkaz:
http://arxiv.org/abs/2410.20791
Autor:
Karaki, Mohammed J., Fahmy, Ahmed E., Williams, Archibald J., Haravifard, Sara, Goldberger, Joshua E., Lu, Yuan-Ming
Topological magnons give rise to possibilities for engineering novel spintronics devices with critical applications in quantum information and computation, due to its symmetry-protected robustness and low dissipation. However, to make reliable and sy
Externí odkaz:
http://arxiv.org/abs/2410.18873
Autor:
Dong, Ximing, Wang, Shaowei, Lin, Dayi, Rajbahadur, Gopi Krishnan, Zhou, Boquan, Liu, Shichao, Hassan, Ahmed E.
Large Language Models excel in tasks like natural language understanding and text generation. Prompt engineering plays a critical role in leveraging LLM effectively. However, LLMs black-box nature hinders its interpretability and effective prompting
Externí odkaz:
http://arxiv.org/abs/2410.13073