Zobrazeno 1 - 10
of 397 088
pro vyhledávání: '"Le An, P."'
Autor:
Souverin, Thierry, Neveu, Jérémy, Betoule, Marc, Bongard, Sébastien, Stubbs, Christopher W., Urbach, Elana, Brownsberger, Sasha, Blanc, Pierre Éric, Tanugi, Johann Cohen, Dagoret-Campagne, Sylvie, Feinstein, Fabrice, Hardin, Delphine, Juramy, Claire, Guillou, Laurent Le, Van Suu, Auguste Le, Moniez, Marc, Plez, Bertrand, Regnault, Nicolas, Sepulveda, Eduardo, Sommer, Kélian
The measurement of type Ia supernovae magnitudes provides cosmological distances, which can be used to constrain dark energy parameters. Large photometric surveys require a substantial improvement in the calibration precision of their photometry to r
Externí odkaz:
http://arxiv.org/abs/2410.24173
Autor:
Le, Cuong Chi, Truong-Vinh, Hoang-Chau, Phan, Huy Nhat, Le, Dung Duy, Nguyen, Tien N., Bui, Nghi D. Q.
Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for code analysis. While these models excel at understanding static syntax, t
Externí odkaz:
http://arxiv.org/abs/2410.23402
In this paper, we first show that increases in beam size of even just small-sized LLM (1B-7B parameters) require an extensive GPU resource consumption, leading to up to 80% of recurring crashes due to memory overloads in LLM-based APR. Seemingly simp
Externí odkaz:
http://arxiv.org/abs/2410.16655
Detecting the presence of anomalies in regression models is a crucial task in machine learning, as anomalies can significantly impact the accuracy and reliability of predictions. Random Sample Consensus (RANSAC) is one of the most popular robust regr
Externí odkaz:
http://arxiv.org/abs/2410.15133
Post-training has emerged as a crucial paradigm for adapting large-scale pre-trained models to various tasks, whose effects are fully reflected by delta parameters (i.e., the disparity between post-trained and pre-trained parameters). While numerous
Externí odkaz:
http://arxiv.org/abs/2410.13841
Currently, it is challenging to investigate aneurismal hemodynamics based on current in-vivo data such as Magnetic Resonance Imaging or Computed Tomography due to the limitations in both spatial and temporal resolutions. In this work, we investigate
Externí odkaz:
http://arxiv.org/abs/2410.12027
Quantum emulators play an important role in the development and testing of quantum algorithms, especially given the limitations of the current FTQC era. Developing high-speed, memory-optimized quantum emulators is a growing research trend, with gate
Externí odkaz:
http://arxiv.org/abs/2410.11146
Despite recent progress, Multi-Object Tracking (MOT) continues to face significant challenges, particularly its dependence on prior knowledge and predefined categories, complicating the tracking of unfamiliar objects. Generic Multiple Object Tracking
Externí odkaz:
http://arxiv.org/abs/2410.09243
Autor:
Tran, Quyen, Le, Minh, Truong, Tuan, Phung, Dinh, Ngo, Linh, Nguyen, Thien, Ho, Nhat, Le, Trung
Drawing inspiration from human learning behaviors, this work proposes a novel approach to mitigate catastrophic forgetting in Prompt-based Continual Learning models by exploiting the relationships between continuously emerging class data. We find tha
Externí odkaz:
http://arxiv.org/abs/2410.04327
Prompt-based techniques, such as prompt-tuning and prefix-tuning, have gained prominence for their efficiency in fine-tuning large pre-trained models. Despite their widespread adoption, the theoretical foundations of these methods remain limited. For
Externí odkaz:
http://arxiv.org/abs/2410.02200