7 citations · 12 across the 6 of their papers we have counts for
6 papers
Knowledge Distillation of Uncertainty using Deep Latent Factor Model
Sehyun Park, Jongjin Lee, Yunseop Shin +2
Deep ensembles deliver state-of-the-art, reliable uncertainty quantification, but their heavy computational and memory requirements hinder their practical deployments to real appli…
Fairness Through Matching
Kunwoong Kim, Insung Kong, Jongjin Lee +3
Group fairness requires that different protected groups, characterized by a given sensitive attribute, receive equal outcomes overall. Typically, the level of group fairness is mea…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Insung Kong, Dongyoon Yang, Jongjin Lee +3
Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications. Particularly, BNNs have the merit of…
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
Dongha Kim, Jaesung Hwang, Jongjin Lee +2
The unsupervised outlier detection (UOD) problem refers to a task to identify inliers given training data which contain outliers as well as inliers, without any labeled information…
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong, Dongyoon Yang, Jongjin Lee +2
As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify su…