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pro vyhledávání: '"Kim, Jiyeon"'
Autor:
Lee, Seongyun, Kim, Geewook, Kim, Jiyeon, Lee, Hyunji, Chang, Hoyeon, Park, Sue Hyun, Seo, Minjoon
Vision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromises the inherent safety capabilities embedded in the original LLMs. Desp
Externí odkaz:
http://arxiv.org/abs/2410.07571
Autor:
Kim, Jiyeon, Lee, Hyunji, Cho, Hyowon, Jang, Joel, Hwang, Hyeonbin, Won, Seungpil, Ahn, Youbin, Lee, Dohaeng, Seo, Minjoon
In this work, we investigate how a model's tendency to broadly integrate its parametric knowledge evolves throughout pretraining, and how this behavior affects overall performance, particularly in terms of knowledge acquisition and forgetting. We int
Externí odkaz:
http://arxiv.org/abs/2410.01380
Publikováno v:
Transactions in GIS, 2024
Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model,
Externí odkaz:
http://arxiv.org/abs/2409.13947
Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account for the d
Externí odkaz:
http://arxiv.org/abs/2404.13858
We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time. We also introduce an efficient inference framework for listwise ranking based on m-ary tournamen
Externí odkaz:
http://arxiv.org/abs/2402.15838
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system. Most of the current G2P systems rely on carefully hand-crafted lexicons developed by experts. This poses a two-fold problem. Firstly, the le
Externí odkaz:
http://arxiv.org/abs/2401.10465
With the recent rapid developments in machine learning (ML), several attempts have been made to apply ML methods to various fluid dynamics problems. However, the feasibility of ML for predicting turbulence dynamics has not yet been explored in detail
Externí odkaz:
http://arxiv.org/abs/2312.07037
Autor:
Jeon, Jaeik, Kim, Jiyeon, Jang, Yeonggul, Yoon, Yeonyee E., Jeong, Dawun, Hong, Youngtaek, Lee, Seung-Ah, Chang, Hyuk-Jae
Doppler echocardiography offers critical insights into cardiac function and phases by quantifying blood flow velocities and evaluating myocardial motion. However, previous methods for automating Doppler analysis, ranging from initial signal processin
Externí odkaz:
http://arxiv.org/abs/2311.08439
Autor:
Jeon, Jaeik, Ha, Seongmin, Jang, Yeonggul, Yoon, Yeonyee E., Kim, Jiyeon, Jeong, Hyunseok, Jeong, Dawun, Hong, Youngtaek, Chang, Seung-Ah Lee Hyuk-Jae
In echocardiographic view classification, accurately detecting out-of-distribution (OOD) data is essential but challenging, especially given the subtle differences between in-distribution and OOD data. While conventional OOD detection methods, such a
Externí odkaz:
http://arxiv.org/abs/2308.16483
Li-ion conductivity is one of the essential properties that determine the performance of cathode materials for Li-ion batteries. Here, using the density functional theory, we investigate the polaron stability and its effect on the Li-ion diffusion in
Externí odkaz:
http://arxiv.org/abs/2211.05464