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pro vyhledávání: '"Lee, Changwoo"'
Large-scale foundation models have demonstrated exceptional performance in language and vision tasks. However, the numerous dense matrix-vector operations involved in these large networks pose significant computational challenges during inference. To
Externí odkaz:
http://arxiv.org/abs/2410.21262
The beta distribution serves as a canonical tool for modelling probabilities in statistics and machine learning. However, there is limited work on flexible and computationally convenient stochastic process extensions for modelling dependent random pr
Externí odkaz:
http://arxiv.org/abs/2402.07048
Autor:
Lee, Changwoo J., Symanski, Elaine, Rammah, Amal, Kang, Dong Hun, Hopke, Philip K., Park, Eun Sug
Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environm
Externí odkaz:
http://arxiv.org/abs/2401.00634
Autor:
Lee, Changwoo J.
Many Bayesian model selection problems, such as variable selection or cluster analysis, start by setting prior model probabilities on a structured model space. Based on a chosen loss function between models, model selection is often performed with a
Externí odkaz:
http://arxiv.org/abs/2311.13347
Autor:
Lee, Changwoo, Kim, Hun-Seok
This paper investigates efficient deep neural networks (DNNs) to replace dense unstructured weight matrices with structured ones that possess desired properties. The challenge arises because the optimal weight matrix structure in popular neural netwo
Externí odkaz:
http://arxiv.org/abs/2310.18882
In this paper, we propose an iterative source error correction (ISEC) decoding scheme for deep-learning-based joint source-channel coding (Deep JSCC). Given a noisy codeword received through the channel, we use a Deep JSCC encoder and decoder pair to
Externí odkaz:
http://arxiv.org/abs/2302.09174
Autor:
Kim, Dasol, Kim, Youngsam, Oh, Jin-Su, Lee, Changwoo, Lim, Hyeonwook, Yang, Cheol-Woong, Sim, Eunji, Cho, Mann-Ho
Reversible conversion over multi-million-times in bond types between metavalent and covalent bonds becomes one of the most promising bases for universal memory. As the conversions have been found in metastable states, extended category of crystal str
Externí odkaz:
http://arxiv.org/abs/2209.14433
The multiple-try Metropolis (MTM) algorithm is an extension of the Metropolis-Hastings (MH) algorithm by selecting the proposed state among multiple trials according to some weight function. Although MTM has gained great popularity owing to its faste
Externí odkaz:
http://arxiv.org/abs/2207.00689
Massive machine type communication (mMTC) has attracted new coding schemes optimized for reliable short message transmission. In this paper, a novel deep learning-based near-orthogonal superposition (NOS) coding scheme is proposed to transmit short m
Externí odkaz:
http://arxiv.org/abs/2206.15065
Autor:
Sakthivelpathi, Vigneshwar, Li, Tianyi, Qian, Zhongjie, Lee, Changwoo, Taylor, Zachary, Chung, Jae-Hyun
Publikováno v:
In Sensors and Actuators: A. Physical 16 October 2024 377
