Zobrazeno 1 - 10
of 3 150
pro vyhledávání: '"Chen, ZhiYong"'
Attention Graph Neural Networks (AT-GNNs), such as GAT and Graph Transformer, have demonstrated superior performance compared to other GNNs. However, existing GNN systems struggle to efficiently train AT-GNNs on GPUs due to their intricate computatio
Externí odkaz:
http://arxiv.org/abs/2411.16127
Autor:
Xue, Nan, Sun, Yaping, Chen, Zhiyong, Tao, Meixia, Xu, Xiaodong, Qian, Liang, Cui, Shuguang, Zhang, Wenjun, Zhang, Ping
Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but the role of wireless networks in supporting LLMs has not been thoroughly explored. In this paper, we propose a wireless distributed Mixtu
Externí odkaz:
http://arxiv.org/abs/2411.06681
In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. I
Externí odkaz:
http://arxiv.org/abs/2410.12302
To obtain better value estimation in reinforcement learning, we propose a novel algorithm based on the double actor-critic framework with temporal difference error-driven regularization, abbreviated as TDDR. TDDR employs double actors, with each acto
Externí odkaz:
http://arxiv.org/abs/2409.19231
Publikováno v:
IEEE Spoken Language Technology Workshop 2024
This paper introduces a novel framework for open-set speaker identification in household environments, playing a crucial role in facilitating seamless human-computer interactions. Addressing the limitations of current speaker models and classificatio
Externí odkaz:
http://arxiv.org/abs/2409.15742
Publikováno v:
The 7th Chinese Conference on Pattern Recognition and Computer Vision PRCV 2024
We introduce StyleFusion-TTS, a prompt and/or audio referenced, style and speaker-controllable, zero-shot text-to-speech (TTS) synthesis system designed to enhance the editability and naturalness of current research literature. We propose a general f
Externí odkaz:
http://arxiv.org/abs/2409.15741
Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves state-of-the-art performance with
Externí odkaz:
http://arxiv.org/abs/2409.16592
Autor:
Chen, Zhiyong
This paper introduces a nonlinear multi-agent dynamic model that characterizes the resource-seizing mechanism for a fixed amount of resources. The model demonstrates a winners-take-all behavior within a zero-sum game framework. It represents one of t
Externí odkaz:
http://arxiv.org/abs/2409.12407
The immersive nature of the metaverse presents significant challenges for wireless multi-user interactive virtual reality (VR), such as ultra-low latency, high throughput and intensive computing, which place substantial demands on the wireless bandwi
Externí odkaz:
http://arxiv.org/abs/2407.20523
In this paper, we address the distributed prescribed-time convex optimization (DPTCO) problem for a class of nonlinear multi-agent systems (MASs) under undirected connected graph. A cascade design framework is proposed such that the DPTCO implementat
Externí odkaz:
http://arxiv.org/abs/2407.11413