5 citations · 12 across the 9 of their papers we have counts for
9 papers
Language-Assisted Feature Transformation for Anomaly Detection
EungGu Yun, Heonjin Ha, Yeongwoo Nam +1
This paper introduces LAFT, a novel feature transformation method designed to incorporate user knowledge and preferences into anomaly detection using natural language. Accurately m…
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models
Taehong Moon, Moonseok Choi, EungGu Yun +4
Diffusion models have shown remarkable performance in generation problems over various domains including images, videos, text, and audio. A practical bottleneck of diffusion models…
On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations
Euna Jung, Jaekeol Choi, EungGu Yun +1
Understanding the encoded representation of Deep Neural Networks (DNNs) has been a fundamental yet challenging objective. In this work, we focus on two possible directions for anal…
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Byung-Hoon Kim, Jungwon Choi, EungGu Yun +3
Graph Transformers have recently been successful in various graph representation learning tasks, providing a number of advantages over message-passing Graph Neural Networks. Utiliz…
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data
Jungwon Choi, Seongho Keum, EungGu Yun +2
Deep neural networks trained on Functional Connectivity (FC) networks extracted from functional Magnetic Resonance Imaging (fMRI) data have gained popularity due to the increasing…
Probabilistic Imputation for Time-series Classification with Missing Data
SeungHyun Kim, Hyunsu Kim, EungGu Yun +3
Multivariate time series data for real-world applications typically contain a significant amount of missing values. The dominant approach for classification with such missing value…
Traversing Between Modes in Function Space for Fast Ensembling
EungGu Yun, Hyungi Lee, Giung Nam +1
Deep ensemble is a simple yet powerful way to improve the performance of deep neural networks. Under this motivation, recent works on mode connectivity have shown that parameters o…
Martingale Posterior Neural Processes
Hyungi Lee, Eunggu Yun, Giung Nam +2
A Neural Process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussi…
Scale Mixtures of Neural Network Gaussian Processes
Hyungi Lee, Eunggu Yun, Hongseok Yang +1
Recent works have revealed that infinitely-wide feed-forward or recurrent neural networks of any architecture correspond to Gaussian processes referred to as Neural Network Gaussia…