Seokho Kang

Knowl. Based Syst., 2024

Training-free approach to constructing ensemble of local experts.

[BibT_eX]

[DOI]

Sunbin Lee

Expert Syst. Appl., 2024

Knowledge distillation with insufficient training data for regression.

[BibT_eX]

[DOI]

Myeonginn Kang

Eng. Appl. Artif. Intell., 2024

Dynamic ensemble of regression neural networks based on predictive uncertainty.

[BibT_eX]

[DOI]

Yoonhyung Lee

Comput. Ind. Eng., 2024

Photovoltaic Cell Defect Detection Based on Weakly Supervised Learning With Module-Level Annotations.

[BibT_eX]

[DOI]

IEEE Access, 2024

Mixup Your Own Latent: Efficient and Robust Self-Supervised Learning on Small Images.

[BibT_eX]

[DOI]

Eugene Yang

Hao Chen

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

Learning from single-defect wafer maps to classify mixed-defect wafer maps.

[BibT_eX]

[DOI]

Expert Syst. Appl., December, 2023

Optimization of missing value imputation for neural networks.

[BibT_eX]

[DOI]

Inf. Sci., November, 2023

Supervised contrastive learning for wafer map pattern classification.

[BibT_eX]

[DOI]

Youngjae Bae

Eng. Appl. Artif. Intell., November, 2023

Surrogate approach to uncertainty quantification of neural networks for regression.

[BibT_eX]

[DOI]

Myeonginn Kang

Appl. Soft Comput., May, 2023

Semi-supervised rotation-invariant representation learning for wafer map pattern analysis.

[BibT_eX]

[DOI]

Hyungu Kang

Eng. Appl. Artif. Intell., April, 2023

Efficient improvement of classification accuracy via selective test-time augmentation.

[BibT_eX]

[DOI]

Jongwook Son

Inf. Sci., 2023

Class-Adaptive Data Augmentation for Image Classification.

[BibT_eX]

[DOI]

Jisu Yoo

IEEE Access, 2023

2022

Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2022

Uncertainty-aware prediction of chemical reaction yields with graph neural networks.

[BibT_eX]

[DOI]

J. Cheminformatics, 2022

ADANOISE: Training neural networks with adaptive noise for imbalanced data classification.

[BibT_eX]

[DOI]

Kyoham Shin

Expert Syst. Appl., 2022

Using binary classifiers for one-class classification.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2022

Dynamic imputation for improved training of neural network with missing values.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2022

Domain-adaptive active learning for cost-effective virtual metrology modeling.

[BibT_eX]

[DOI]

Comput. Ind., 2022

Semi-automatic wafer map pattern classification with convolutional neural networks.

[BibT_eX]

[DOI]

Suhee Yoon

Comput. Ind. Eng., 2022

2021

Product failure prediction with missing data using graph neural networks.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2021

Active learning with missing values considering imputation uncertainty.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2021

MI-MOTE: Multiple imputation-based minority oversampling technique for imbalanced and incomplete data classification.

[BibT_eX]

[DOI]

Kyoham Shin

Inf. Sci., 2021

Active cluster annotation for wafer map pattern classification in semiconductor manufacturing.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2021

Data-free knowledge distillation in neural networks for regression.

[BibT_eX]

[DOI]

Myeonginn Kang

Expert Syst. Appl., 2021

A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification.

[BibT_eX]

[DOI]

Hyungu Kang

Comput. Ind., 2021

Model-Agnostic Post-Processing Based on Recursive Feedback for Medical Image Segmentation.

[BibT_eX]

[DOI]

Jaeho Kim

IEEE Access, 2021

2020

Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing.

[BibT_eX]

[DOI]

J. Intell. Manuf., 2020

Neural Message Passing for NMR Chemical Shift Prediction.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2020

Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2020

Compressed graph representation for scalable molecular graph generation.

[BibT_eX]

[DOI]

J. Cheminformatics, 2020

Expected margin-based pattern selection for support vector machines.

[BibT_eX]

[DOI]

Dongil Kim

Expert Syst. Appl., 2020

Model validation failure in class imbalance problems.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2020

Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks.

[BibT_eX]

[DOI]

IEEE Access, 2020

Unsupervised Anomaly Detection Using Style Distillation.

[BibT_eX]

[DOI]

IEEE Access, 2020

Kernel Rotation Forests for Classification.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing, 2020

2019

Clustering-based proxy measure for optimizing one-class classifiers.

[BibT_eX]

[DOI]

Jaehong Yu

Pattern Recognit. Lett., 2019

Conditional Molecular Design with Deep Generative Models.

[BibT_eX]

[DOI]

Kyunghyun Cho

J. Chem. Inf. Model., 2019

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation.

[BibT_eX]

[DOI]

J. Cheminformatics, 2019

Rapid fault cause identification in surface mount technology processes based on factory-wide data analysis.

[BibT_eX]

[DOI]

Int. J. Distributed Sens. Networks, 2019

Molecular geometry prediction using a deep generative graph neural network.

[BibT_eX]

[DOI]

CoRR, 2019

Approximate training of one-class support vector machines using expected margin.

[BibT_eX]

[DOI]

Dongil Kim

Comput. Ind. Eng., 2019

2018

Locally linear ensemble for regression.

[BibT_eX]

[DOI]

Pilsung Kang

Inf. Sci., 2018

Product failure prediction with missing data.

[BibT_eX]

[DOI]

Int. J. Prod. Res., 2018

Regression with re-labeling for noisy data.

[BibT_eX]

[DOI]

Youngdoo Son

Expert Syst. Appl., 2018

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks.

[BibT_eX]

[DOI]

Artif. Intell. Medicine, 2018

2017

Reliable prediction of anti-diabetic drug failure using a reject option.

[BibT_eX]

[DOI]

Pattern Anal. Appl., 2017

Ranking process parameter association with low yield wafers using spec-out event network analysis.

[BibT_eX]

[DOI]

Comput. Ind. Eng., 2017

Virtual metrology for copper-clad laminate manufacturing.

[BibT_eX]

[DOI]

Comput. Ind. Eng., 2017

Mining the relationship between production and customer service data for failure analysis of industrial products.

[BibT_eX]

[DOI]

Comput. Ind. Eng., 2017

2015

Improvement of virtual metrology performance by removing metrology noises in a training dataset.

[BibT_eX]

[DOI]

Pattern Anal. Appl., 2015

Constructing a multi-class classifier using one-against-one approach with different binary classifiers.

[BibT_eX]

[DOI]

Pilsung Kang

Neurocomputing, 2015

Optimal construction of one-against-one classifier based on meta-learning.

[BibT_eX]

[DOI]

Neurocomputing, 2015

A novel multi-class classification algorithm based on one-class support vector machine.

[BibT_eX]

[DOI]

Intell. Data Anal., 2015

An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2015

Multi-class classification via heterogeneous ensemble of one-class classifiers.

[BibT_eX]

[DOI]

Pilsung Kang

Eng. Appl. Artif. Intell., 2015

2014

Knowledge discovery in inspection reports of marine structures.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2014

Approximating support vector machine with artificial neural network for fast prediction.

[BibT_eX]

[DOI]