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Found 5 papers, 1 papers with code
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks
While recent advancements in commercial large language models (LM) have shown promising results in medical tasks, their closed-source nature poses significant privacy and security concerns, hindering their widespread use in the medical field.
Pragmatic Competence Evaluation of Large Language Models for Korean
The current evaluation of Large Language Models (LLMs) predominantly relies on benchmarks focusing on their embedded knowledge by testing through multiple-choice questions (MCQs), a format inherently suited for automated evaluation.
Conditional Temporal Neural Processes with Covariance Loss
We introduce a novel loss function, Covariance Loss, which is conceptually equivalent to conditional neural processes and has a form of regularization so that is applicable to many kinds of neural networks.
Ranked #1 on
Time Series Forecasting
on PeMSD7
Improving seasonal forecast using probabilistic deep learning
We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system.
Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM
Polluting fine dusts in South Korea which are mainly consisted of biomass burning and fugitive dust blown from dust belt is significant problem these days.
