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pro vyhledávání: '"Lee, In Hyung"'
Two-dimensional color codes are a promising candidate for fault-tolerant quantum computing, as they have high encoding rates, transversal implementation of logical Clifford gates, and high feasibility of magic state constructions. However, decoding c
Externí odkaz:
http://arxiv.org/abs/2404.07482
Autor:
Lee, Jin Hyung, Lee, Ben Seiyon
Gaussian and discrete non-Gaussian spatial datasets are prevalent across many fields such as public health, ecology, geosciences, and social sciences. Bayesian spatial generalized linear mixed models (SGLMMs) are a flexible class of models designed f
Externí odkaz:
http://arxiv.org/abs/2402.15705
The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks. However, integr
Externí odkaz:
http://arxiv.org/abs/2402.11656
In this article, we propose a multi-agent deep reinforcement learning (MADRL) framework to train a multiple access protocol for downlink low earth orbit (LEO) satellite networks. By improving the existing learned protocol, emergent random access chan
Externí odkaz:
http://arxiv.org/abs/2402.02350
Hybridizing different degrees of freedom or physical platforms potentially offers various advantages in building scalable quantum architectures. We here introduce a fault-tolerant hybrid quantum computation by taking the advantages of both discrete v
Externí odkaz:
http://arxiv.org/abs/2401.00450
Autor:
Lee, Ju-Hyung, Molisch, Andreas F.
Large-scale channel prediction, i.e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning. Ray tracing (RT)-based methods have been widely used for many years, but they requ
Externí odkaz:
http://arxiv.org/abs/2312.03950
Mixed-precision quantization of efficient networks often suffer from activation instability encountered in the exploration of bit selections. To address this problem, we propose a novel method called MetaMix which consists of bit selection and weight
Externí odkaz:
http://arxiv.org/abs/2311.06798
This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks' HO procedures. D
Externí odkaz:
http://arxiv.org/abs/2310.20215
Autor:
Fabbro, Giorgio, Uhlich, Stefan, Lai, Chieh-Hsin, Choi, Woosung, Martínez-Ramírez, Marco, Liao, Weihsiang, Gadelha, Igor, Ramos, Geraldo, Hsu, Eddie, Rodrigues, Hugo, Stöter, Fabian-Robert, Défossez, Alexandre, Luo, Yi, Yu, Jianwei, Chakraborty, Dipam, Mohanty, Sharada, Solovyev, Roman, Stempkovskiy, Alexander, Habruseva, Tatiana, Goswami, Nabarun, Harada, Tatsuya, Kim, Minseok, Lee, Jun Hyung, Dong, Yuanliang, Zhang, Xinran, Liu, Jiafeng, Mitsufuji, Yuki
Publikováno v:
Transactions of the International Society for Music Information Retrieval, 7(1), pp.63-84, 2024
This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music source separation (MSS), i.e., training MSS models in the presence of er
Externí odkaz:
http://arxiv.org/abs/2308.06979
Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification. This hinders researchers from fully utilizing public-available models in discourse analysis. This
Externí odkaz:
http://arxiv.org/abs/2307.03378