데싸(Data Science) 노트

논문명 - An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale 게재 일자 - 2021년 6월 3일 URL 링크 - https://arxiv.org/pdf/2010.11929.pdf Abstract 1. Introduction 2. Related Work 3. Method 3.1. Vision Transformer (ViT) 3.2. Fine-Tuning and Higher Resolution 4. Experiments 4.1. Setup 4.2. Comparison to State of the Art 4.3. Pre-Training Data Requirements 4.4. Scaling Study 4.5. Inspectin..

논문명 - ICNet for Real-Time Semantic Segmentation on High-Resolution Images 저자 - Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, Jiaya Jia 게재 일자 - 2018년 8월 20일 URL 링크 - https://arxiv.org/pdf/1704.08545.pdf 1. Introduction 1.1. Status of Fast Semantic Segmentation 1.2. Our Focus and Contributions 2. Related Work 2.1. High Quality Semantic Segmentation 2.2. High Efficiency Semantic Segmen..

논문 명 - 추천 시스템 기법 연구동향 분석 (Review and Analysis of Recommender Systems) 저자 - 손지은, 김성범, 김현중, 조성준 게재 저널 - 대한산업공학회지 게재 일자 - 2015년 4월 15일 URL 링크 - http://koreascience.or.kr/article/JAKO201512053817215.page Review and Analysis of Recommender Systems -Journal of Korean Institute of Industrial Engineers | Korea Science Abstract The explosive growth of the world-wide-web and the emergence of e-commerce ha..

저자 Ledig et al. 게재 저널 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE 2017) 게재 날짜 2017년 URL https://openaccess.thecvf.com/content_cvpr_2017/html/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.html CVPR 2017 Open Access Repository Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar..

저자 Karen Stengel et al. 게재 저널 PNAS (Proceedings of the National Academy of Sciences of the United States of America) 게재 날짜 2020년 07월 21일 URL https://www.pnas.org/content/117/29/16805.short Adversarial super-resolution of climatological wind and solar data Global climate simulations are typically unable to resolve wind and solar data at a resolution sufficient for renewable energy resource assess..

논문 명: LightGBM: A Highly Efficient Gradient Boosting Decision Tree 저자 명: Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu 게재 저널: NIPS 2017 (31st Conference on Neural Information Processing Systems) 게재 날짜: 2017년 URL: http://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision LightGBM: A Highly Efficient Gradient Boosting Dec..

논문 명: U-Net: Convolutional Networks for Biomedical Image Segmentation 저자 명: Olaf Ronneberger, Philipp Fischer, and Thomas Brox 게재 저널: International Conference on Medical Image Computing and Computer-Assisted Intervention 게재 날짜: 2015년 5월 18일 URL: https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28