14 citations · 36 across the 16 of their papers we have counts for
16 papers
Nearest Neighbor Guidance for Out-of-Distribution Detection
Jaewoo Park, Yoon Gyo Jung, Andrew Beng Jin Teoh
Detecting out-of-distribution (OOD) samples are crucial for machine learning models deployed in open-world environments. Classifier-based scores are a standard approach for OOD det…
Fast Computer Model Calibration using Annealed and Transformed Variational Inference
Dongkyu Derek Cho, Won Chang, Jaewoo Park
Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameter…
ASAP: Accurate semantic segmentation for real time performance
Jaehyun Park, Subin Lee, Eon Kim +4
Feature fusion modules from encoder and self-attention module have been adopted in semantic segmentation. However, the computation of these modules is costly and has operational li…
A Bayesian Convolutional Neural Network-based Generalized Linear Model
Yeseul Jeon, Won Chang, Seonghyun Jeong +2
Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data.…
Understanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation
Jaewoo Park, Hojin Park, Eunju Jeong +1
The findings on open-set recognition (OSR) show that models trained on classification datasets are capable of detecting unknown classes not encountered during the training process.…
A Spatio-Temporal Dirichlet Process Mixture Model for Coronavirus Disease-19
Jaewoo Park, Seorim Yi, Won Chang +1
Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide r…
PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for Autonomous Car using Point Convolution and Sparse Convolution Network
Jaehyun Park, Chansoo Kim, Kichun Jo
The autonomous car must recognize the driving environment quickly for safe driving. As the Light Detection And Range (LiDAR) sensor is widely used in the autonomous car, fast seman…
DProST: Dynamic Projective Spatial Transformer Network for 6D Pose Estimation
Jaewoo Park, Nam Ik Cho
Predicting the object's 6D pose from a single RGB image is a fundamental computer vision task. Generally, the distance between transformed object vertices is employed as an objecti…
An Interaction Neyman-Scott Point Process Model for Coronavirus Disease-19
J. Park, W. Chang, B. Choi
With rapid transmission, the coronavirus disease 2019 (COVID-19) has led to over 2 million deaths worldwide, posing significant societal challenges. Understanding the spatial patte…
A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets
Benjamin Seiyon Lee, Jaewoo Park
Nonstationary non-Gaussian spatial data are common in many disciplines, including climate science, ecology, epidemiology, and social sciences. Examples include count data on diseas…
Bayesian Indirect Inference for Models with Intractable Normalizing Functions
Jaewoo Park
Inference for doubly intractable distributions is challenging because the intractable normalizing functions of these models include parameters of interest. Previous auxiliary varia…
Bayesian Shrinkage for Functional Network Models, with Applications to Longitudinal Item Response Data
Jaewoo Park, Yeseul Jeon, Minsuk Shin +2
Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is…
Reduced-dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models
Jaewoo Park, Murali Haran
Monte Carlo maximum likelihood (MCML) provides an elegant approach to find maximum likelihood estimators (MLEs) for latent variable models. However, MCML algorithms are computation…
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
Uiwon Hwang, Jaewoo Park, Hyemi Jang +2
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise the network at the inferen…
A Function Emulation Approach for Doubly Intractable Distributions
Jaewoo Park, Murali Haran
Doubly intractable distributions arise in many settings, for example in Markov models for point processes and exponential random graph models for networks. Bayesian inference for t…
Bayesian Inference in the Presence of Intractable Normalizing Functions
Jaewoo Park, Murali Haran
Models with intractable normalizing functions arise frequently in statistics. Common examples of such models include exponential random graph models for social networks and Markov…