Zobrazeno 1 - 10
of 132 863
pro vyhledávání: '"Bâ, A."'
Publikováno v:
IEEE Transactions on Signal Processing, vol. 72, 2024, pp. 4888-4917
This article presents the Labeled Random Finite Set (LRFS) framework for multi-object systems-systems in which the number of objects and their states are unknown and vary randomly with time. In particular, we focus on state and trajectory estimation
Externí odkaz:
http://arxiv.org/abs/2409.18531
When we detect communities in temporal networks it is important to ask questions about how they change in time. Normalised Mutual Information (NMI) has been used to measure the similarity of communities when the nodes on a network do not change. We p
Externí odkaz:
http://arxiv.org/abs/2411.10632
In this paper, we propose the development of an interactive platform between humans and a dual-arm robotic system based on the Robot Operating System (ROS) and a multimodal artificial intelligence model. Our proposed platform consists of two main com
Externí odkaz:
http://arxiv.org/abs/2411.05342
In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be en
Externí odkaz:
http://arxiv.org/abs/2411.04419
MPI_Alltoallv generalizes the uniform all-to-all communication (MPI_Alltoall) by enabling the exchange of data blocks of varied sizes among processes. This function plays a crucial role in many applications, such as FFT computation and relational alg
Externí odkaz:
http://arxiv.org/abs/2411.02581
To enhance the generalization of machine learning models to unseen data, techniques such as dropout, weight decay ($L_2$ regularization), and noise augmentation are commonly employed. While regularization methods (i.e., dropout and weight decay) are
Externí odkaz:
http://arxiv.org/abs/2410.14602
As machine learning models continue to swiftly advance, calibrating their performance has become a major concern prior to practical and widespread implementation. Most existing calibration methods often negatively impact model accuracy due to the lac
Externí odkaz:
http://arxiv.org/abs/2410.10864
Autor:
Zhao, Ranchi, Thai, Zhen Leng, Zhang, Yifan, Hu, Shengding, Ba, Yunqi, Zhou, Jie, Cai, Jie, Liu, Zhiyuan, Sun, Maosong
Publikováno v:
EMNLP 2024
The performance of Large Language Models (LLMs) is substantially influenced by the pretraining corpus, which consists of vast quantities of unsupervised data processed by the models. Despite its critical role in model performance, ensuring the qualit
Externí odkaz:
http://arxiv.org/abs/2410.05639
Haptic feedback is essential for dexterous telemanipulation that enables operators to control robotic hands remotely with high skill and precision, mimicking a human hand's natural movement and sensation. However, current haptic methods for dexterous
Externí odkaz:
http://arxiv.org/abs/2409.20527
In the modern era of rapidly increasing data volumes, accurately retrieving and recommending relevant documents has become crucial in enhancing the reliability of Question Answering (QA) systems. Recently, Retrieval Augmented Generation (RAG) has gai
Externí odkaz:
http://arxiv.org/abs/2409.13699