| A deep neural network for classification of melt-pool images in metal additive manufacturing O Kwon, HG Kim, MJ Ham, W Kim, GH Kim, JH Cho, NI Kim, K Kim Journal of Intelligent Manufacturing, 1-12, 2018 | 107 | 2018 |
| Stable Forecasting of Environmental Time Series via Long Short Term Memory Recurrent Neural Network K Kim, DK Kim, J Noh, M Kim IEEE Access 6, 75216-75228, 2018 | 29 | 2018 |
| Probabilistic model building in genetic programming: a critical review K Kim, Y Shan, XH Nguyen, RI McKay Genetic Programming and Evolvable Machines 15 (2), 115-167, 2014 | 28 | 2014 |
| Improving LSTM CRFs using character-based compositions for Korean named entity recognition SH Na, H Kim, J Min, K Kim Computer Speech & Language 54, 106-121, 2019 | 24 | 2019 |
| A Convolutional Neural Network for Prediction of Laser Power Using Melt-Pool Images in Laser Powder Bed Fusion O Kwon, HG Kim, W Kim, GH Kim, K Kim IEEE Access 8, 23255-23263, 2020 | 15 | 2020 |
| Prediction of the mortality risk in peritoneal dialysis patients using machine learning models: a nation-wide prospective cohort in Korea J Noh, KD Yoo, W Bae, JS Lee, K Kim, JH Cho, H Lee, DK Kim, CS Lim, ... Scientific reports 10 (1), 1-11, 2020 | 11 | 2020 |
| Classification-Based Approach for Hybridizing Statistical and Rule-Based Machine Translation EJ Park, OW Kwon, K Kim, YK Kim ETRI Journal 37 (3), 541-550, 2015 | 9 | 2015 |
| Stochastic diversity loss and scalability in estimation of distribution genetic programming K Kim, RI McKay IEEE Transactions on Evolutionary Computation 17 (3), 301-320, 2013 | 9 | 2013 |
| Stochastic diversity loss and scalability in estimation of distribution genetic programming K Kim, RI McKay IEEE Transactions on Evolutionary Computation 17 (3), 301-320, 2013 | 9 | 2013 |
| Operator self-adaptation in genetic programming MH Kim, RIB McKay, NX Hoai, K Kim Genetic Programming, 215-226, 2011 | 8 | 2011 |
| Improving stack LSTMs by combining syllables and morphemes for korean dependency parsing SH Na, JH Shin, K Kim 한국어정보학회: 학술대회논문집, 9-13, 2016 | 7 | 2016 |
| Sampling bias in estimation of distribution algorithms for genetic programming using prototype trees K Kim, BRI McKay, D Punithan Pacific Rim International Conference on Artificial Intelligence, 100-111, 2010 | 7 | 2010 |
| Line Chart understanding with convolutional neural network C Sohn, H Choi, K Kim, J Park, J Noh Electronics 10 (6), 749, 2021 | 5 | 2021 |
| Verbosity normalized pseudo-relevance feedback in information retrieval SH Na, K Kim Information Processing & Management 54 (2), 219-239, 2018 | 5 | 2018 |
| Investigating vesicular selection: A selection operator in in vitro evolution YG Lee, B McKay, KI Kim, DK Kim, NX Hoai Applied Soft Computing 11 (8), 5528-5550, 2011 | 5 | 2011 |
| Recursion-based biases in stochastic grammar model genetic programming K Kim, RIB McKay, NX Hoai IEEE Transactions on Evolutionary Computation 20 (1), 81-95, 2016 | 4 | 2016 |
| Center-shared sliding ensemble of neural networks for syntax analysis of natural language K Kim, Y Jin, S Nah, YK Kim Expert Systems with Applications 83, 215–225, 2017 | 3 | 2017 |
| Divergence-based fine pruning of phrase-based statistical translation model K Kim, EJ Park, J Shin, OW Kwon, YK Kim Computer Speech and Language, 2016 | 3 | 2016 |
| Time-sensitive adaptation of regularization strength of recurrent neural networks for accurate learning K Kim 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 2 | 2017 |
| Cutting evaluation costs: An investigation into early termination in genetic programming N Park, K Kim, RI McKay 2013 IEEE Congress on Evolutionary Computation, 3291-3298, 2013 | 2 | 2013 |
Bob McKayHonorary Professor of Computer Science, Australian National Universtyanu.edu.au의 이메일 확인됨
