Zobrazeno 1 - 10
of 4 947
pro vyhledávání: '"Sejin An"'
Publikováno v:
Annals of Pediatric Endocrinology & Metabolism, Vol 29, Iss 3, Pp 182-190 (2024)
Purpose We assessed the clinical relevance of waist-height ratio (WHtR) as an indicator of cardiometabolic risk and body fat mass measured by dual-energy x-ray absorptiometry (DXA) among Korean children and adolescents. Methods Data from 1,661 childr
Externí odkaz:
https://doaj.org/article/d7d299cb6d844235834a4d5243e797ef
Autor:
Lee, Jaewoong, Woo, Junhee, Kim, Sejin, Paulina, Cinthya, Park, Hyunmin, Kim, Hee-Tak, Park, Steve, Kim, Jihan
Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce a novel multi modal data-driven approach employing an Automatic Battery dat
Externí odkaz:
http://arxiv.org/abs/2411.17625
Goal-oriented chatbots are essential for automating user tasks, such as booking flights or making restaurant reservations. A key component of these systems is Dialogue State Tracking (DST), which interprets user intent and maintains the dialogue stat
Externí odkaz:
http://arxiv.org/abs/2410.22767
We propose a novel offline reinforcement learning (offline RL) approach, introducing the Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation (DIAR) framework. We address two key challenges in offline RL: out-of-distribution samples a
Externí odkaz:
http://arxiv.org/abs/2410.11338
Effective long-term strategies enable AI systems to navigate complex environments by making sequential decisions over extended horizons. Similarly, reinforcement learning (RL) agents optimize decisions across sequences to maximize rewards, even witho
Externí odkaz:
http://arxiv.org/abs/2410.11324
Autor:
Kim, Sejin, Kim, Sundong
While significant progress has been made in task-specific applications, current models struggle with deep reasoning, generality, and adaptation -- key components of System 2 reasoning that are crucial for achieving Artificial General Intelligence (AG
Externí odkaz:
http://arxiv.org/abs/2410.07866
We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function $M(F^2)$, which is nece
Externí odkaz:
http://arxiv.org/abs/2410.06523
The effectiveness of AI model training hinges on the quality of the trajectory data used, particularly in aligning the model's decision with human intentions. However, in the human task-solving trajectories, we observe significant misalignments betwe
Externí odkaz:
http://arxiv.org/abs/2409.14191
This paper demonstrates that model-based reinforcement learning (model-based RL) is a suitable approach for the task of analogical reasoning. We hypothesize that model-based RL can solve analogical reasoning tasks more efficiently through the creatio
Externí odkaz:
http://arxiv.org/abs/2408.14855
Autor:
Kim, Joon, Park, Sejin
Federated Learning(FL), in theory, preserves privacy of individual clients' data while producing quality machine learning models. However, attacks such as Deep Leakage from Gradients(DLG) severely question the practicality of FL. In this paper, we em
Externí odkaz:
http://arxiv.org/abs/2408.08430