| Deep Learning in Bioinformatics S Min, B Lee, S Yoon Briefings in Bioinformatics 18 (5), 851-869, 2016 | 1776 | 2016 |
| ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models J Choi, S Kim, Y Jeong, Y Gwon, S Yoon ICCV 2021 Oral (arXiv preprint arXiv:2108.02938), 2021 | 506* | 2021 |
| FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference J Lee, E Kim, S Lee, J Lee, S Yoon IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5267-5276, 2019 | 499 | 2019 |
| Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search J Kim, S Kim, J Kong, S Yoon NeurIPS 2020 Oral (arXiv preprint arXiv:2005.11129), 2020 | 444 | 2020 |
| Patch SVDD: Patch-level SVDD for anomaly detection and segmentation J Yi, S Yoon Proceedings of the Asian Conference on Computer Vision, 2020 | 433 | 2020 |
| How generative adversarial networks and their variants work: An overview Y Hong, U Hwang, J Yoo, S Yoon ACM Computing Surveys (CSUR) 52 (1), 1-43, 2019 | 384 | 2019 |
| Spiking-YOLO: Spiking Neural Network for Real-time Object Detection S Kim, S Park, B Na, S Yoon AAAI 2020 (arXiv preprint arXiv:1903.06530), 2020 | 357* | 2020 |
| RNA design rules from a massive open laboratory J Lee, W Kladwang, M Lee, D Cantu, M Azizyan, H Kim, A Limpaecher, ... Proceedings of the National Academy of Sciences 111 (6), 2122-2127, 2014 | 350 | 2014 |
| Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines K Choi, J Yi, C Park, S Yoon IEEE access 9, 120043-120065, 2021 | 312 | 2021 |
| Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity HK Kim, S Min, M Song, S Jung, JW Choi, Y Kim, S Lee, S Yoon, H Kim Nature Biotechnology 36 (3), 239-241, 2018 | 308 | 2018 |
| Got target?: computational methods for microRNA target prediction and their extension H Min, S Yoon Experimental & molecular medicine 42 (4), 233-244, 2010 | 256 | 2010 |
| Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation J Lee, E Kim, S Yoon CVPR 2021 (arXiv preprint arXiv:2103.08896), 2021 | 224 | 2021 |
| SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance HK Kim, Y Kim, S Lee, S Min, JY Bae, JW Choi, J Park, D Jung, S Yoon, ... Science advances 5 (11), eaax9249, 2019 | 216 | 2019 |
| FloWaveNet : A Generative Flow for Raw Audio S Kim, S Lee, J Song, S Yoon ICML 2019 (arXiv preprint arXiv:1811.02155), 2018 | 202 | 2018 |
| Predicting the efficiency of prime editing guide RNAs in human cells HK Kim, G Yu, J Park, S Min, S Lee, S Yoon, HH Kim Nature Biotechnology 39 (2), 198-206, 2021 | 198 | 2021 |
| Prediction of regulatory modules comprising microRNAs and target genes S Yoon, G De Micheli Bioinformatics 21 (suppl_2), ii93-ii100, 2005 | 192 | 2005 |
| Comprehensive ensemble in QSAR prediction for drug discovery S Kwon, H Bae, J Jo, S Yoon BMC bioinformatics 20, 1-12, 2019 | 178 | 2019 |
| BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation J Lee, J Yi, C Shin, S Yoon CVPR 2021 (arXiv preprint arXiv:2103.08907), 2021 | 167 | 2021 |
| Prediction of the sequence-specific cleavage activity of Cas9 variants N Kim, HK Kim, S Lee, JH Seo, JW Choi, J Park, S Min, S Yoon, SR Cho, ... Nature Biotechnology 38 (11), 1328-1336, 2020 | 167 | 2020 |
| Big/little deep neural network for ultra low power inference E Park, D Kim, S Kim, YD Kim, G Kim, S Yoon, S Yoo 2015 international conference on hardware/software codesign and system …, 2015 | 166 | 2015 |
Byunghan LeeSeoul National University of Science and Technology在 seoultech.ac.kr 的电子邮件经过验证
