Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Chen, Xingran"'
With the rising demand for high-rate and timely communications, fog radio access networks (F-RANs) offer a promising solution. This work investigates age of information (AoI) performance in F-RANs, consisting of multiple content users (CUs), enhanced
Externí odkaz:
http://arxiv.org/abs/2407.02930
We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a multi-hop wireless network with statistically-identical agents. Agents cache the most recent samples from others and communicate over wireless coll
Externí odkaz:
http://arxiv.org/abs/2404.03227
In communication and computation cooperative networks (3CNs), timely computation is crucial but not always guaranteed. There is a strong demand for a computational task to be completed within a given deadline. The time taken involves both processing
Externí odkaz:
http://arxiv.org/abs/2403.05007
Autor:
Ma, Yinghao, Yuan, Ruibin, Li, Yizhi, Zhang, Ge, Chen, Xingran, Yin, Hanzhi, Lin, Chenghua, Benetos, Emmanouil, Ragni, Anton, Gyenge, Norbert, Liu, Ruibo, Xia, Gus, Dannenberg, Roger, Guo, Yike, Fu, Jie
Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trai
Externí odkaz:
http://arxiv.org/abs/2307.05161
Autor:
Yuan, Ruibin, Ma, Yinghao, Li, Yizhi, Zhang, Ge, Chen, Xingran, Yin, Hanzhi, Zhuo, Le, Liu, Yiqi, Huang, Jiawen, Tian, Zeyue, Deng, Binyue, Wang, Ningzhi, Lin, Chenghua, Benetos, Emmanouil, Ragni, Anton, Gyenge, Norbert, Dannenberg, Roger, Chen, Wenhu, Xia, Gus, Xue, Wei, Liu, Si, Wang, Shi, Liu, Ruibo, Guo, Yike, Fu, Jie
In the era of extensive intersection between art and Artificial Intelligence (AI), such as image generation and fiction co-creation, AI for music remains relatively nascent, particularly in music understanding. This is evident in the limited work on
Externí odkaz:
http://arxiv.org/abs/2306.10548
Due to Multilingual Neural Machine Translation's (MNMT) capability of zero-shot translation, many works have been carried out to fully exploit the potential of MNMT in zero-shot translation. It is often hypothesized that positional information may hi
Externí odkaz:
http://arxiv.org/abs/2305.19857
Autor:
Li, Yizhi, Yuan, Ruibin, Zhang, Ge, Ma, Yinghao, Chen, Xingran, Yin, Hanzhi, Xiao, Chenghao, Lin, Chenghua, Ragni, Anton, Benetos, Emmanouil, Gyenge, Norbert, Dannenberg, Roger, Liu, Ruibo, Chen, Wenhu, Xia, Gus, Shi, Yemin, Huang, Wenhao, Wang, Zili, Guo, Yike, Fu, Jie
Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application t
Externí odkaz:
http://arxiv.org/abs/2306.00107
Autor:
Cui, Wanyun, Chen, Xingran
Previous research has demonstrated that natural language explanations provide valuable inductive biases that guide models, thereby improving the generalization ability and data efficiency. In this paper, we undertake a systematic examination of the e
Externí odkaz:
http://arxiv.org/abs/2305.15520
Autor:
Cui, Wanyun, Chen, Xingran
Recent years have witnessed the emergence of textual commonsense knowledge bases, aimed at providing more nuanced and context-rich knowledge. The integration of external commonsense into language models has been shown to be a key enabler in advancing
Externí odkaz:
http://arxiv.org/abs/2305.15516
Is there a unified framework for graph-based retrosynthesis prediction? Through analysis of full-, semi-, and non-template retrosynthesis methods, we discovered that they strive to strike an optimal balance between combinability and consistency: \tex
Externí odkaz:
http://arxiv.org/abs/2305.15153