Zobrazeno 1 - 10
of 4 236
pro vyhledávání: '"Tran, A. H."'
Autor:
Piran, Md. Jalil, Tran, Nguyen H.
Following a brief introduction to research, research processes, research types, papers, reviews, and evaluations, this paper presents a structured framework for addressing inconsistencies in research methodology, technical writing, quality assessment
Externí odkaz:
http://arxiv.org/abs/2412.05683
Publikováno v:
38th Conference on Neural Information Processing Systems (NeurIPS 2024
This paper aims at developing novel shuffling gradient-based methods for tackling two classes of minimax problems: nonconvex-linear and nonconvex-strongly concave settings. The first algorithm addresses the nonconvex-linear minimax model and achieves
Externí odkaz:
http://arxiv.org/abs/2410.22297
Autor:
Nguyen, Minh Duong, Le, Khanh, Do, Khoi, Tran, Nguyen H., Nguyen, Duc, Trinh, Chien, Yang, Zhaohui
In personalized Federated Learning (pFL), high data heterogeneity can cause significant gradient divergence across devices, adversely affecting the learning process. This divergence, especially when gradients from different users form an obtuse angle
Externí odkaz:
http://arxiv.org/abs/2410.02845
Autor:
Tran, Quang H., Bowler, Brendan P.
The recent development of statistical methods that can distinguish between stellar activity and dynamical signals in radial velocity (RV) observations has facilitated the discovery and characterization of planets orbiting young stars. One such techni
Externí odkaz:
http://arxiv.org/abs/2409.15407
Neural functional networks (NFNs) have recently gained significant attention due to their diverse applications, ranging from predicting network generalization and network editing to classifying implicit neural representation. Previous NFN designs oft
Externí odkaz:
http://arxiv.org/abs/2409.11697
Autor:
Wang, Xingyao, Li, Boxuan, Song, Yufan, Xu, Frank F., Tang, Xiangru, Zhuge, Mingchen, Pan, Jiayi, Song, Yueqi, Li, Bowen, Singh, Jaskirat, Tran, Hoang H., Li, Fuqiang, Ma, Ren, Zheng, Mingzhang, Qian, Bill, Shao, Yanjun, Muennighoff, Niklas, Zhang, Yizhe, Hui, Binyuan, Lin, Junyang, Brennan, Robert, Peng, Hao, Ji, Heng, Neubig, Graham
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there ha
Externí odkaz:
http://arxiv.org/abs/2407.16741
Autor:
Nguyen, Tung-Anh, Le, Long Tan, Nguyen, Tuan Dung, Bao, Wei, Seneviratne, Suranga, Hong, Choong Seon, Tran, Nguyen H.
Publikováno v:
IEEE/ACM Transactions on Networking On page(s): 1-16 Print ISSN: 1063-6692 Online ISSN: 1558-2566 Digital Object Identifier: 10.1109/TNET.2024.3423780
With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activities. While traditional machine learning-based intrusion detection sys
Externí odkaz:
http://arxiv.org/abs/2407.07421
Autor:
Franson, Kyle, Balmer, William O., Bowler, Brendan P., Pueyo, Laurent, Zhou, Yifan, Rickman, Emily, Zhang, Zhoujian, Mukherjee, Sagnick, Pearce, Tim D., Gagliuffi, Daniella C. Bardalez, Biddle, Lauren I., Brandt, Timothy D., Bowens-Rubin, Rachel, Crepp, Justin R., Davidson, Jr., James W., Faherty, Jacqueline, Ginski, Christian, Horch, Elliott P., Morgan, Marvin, Morley, Caroline V., Perrin, Marshall D., Sanghi, Aniket, Salama, Maissa, Theissen, Christopher A., Tran, Quang H., Wolf, Trevor N.
With a dynamical mass of $3 \, M_\mathrm{Jup}$, the recently discovered giant planet AF Lep b is the lowest-mass imaged planet with a direct mass measurement. Its youth and spectral type near the L/T transition make it a promising target to study the
Externí odkaz:
http://arxiv.org/abs/2406.09528
While astonishingly capable, large Language Models (LLM) can sometimes produce outputs that deviate from human expectations. Such deviations necessitate an alignment phase to prevent disseminating untruthful, toxic, or biased information. Traditional
Externí odkaz:
http://arxiv.org/abs/2405.15230
Beyond class frequency, we recognize the impact of class-wise relationships among various class-specific predictions and the imbalance in label masks on long-tailed segmentation learning. To address these challenges, we propose an innovative Pixel-wi
Externí odkaz:
http://arxiv.org/abs/2404.05393