Self-Organising Maps (SOM) are Artificial Neural Networks used in Pattern Recognition tasks. Their major advantage over other architectures is human readability of a model. However, they often gain poorer accuracy. Mostly used metric in SOM is the Euclidean distance, which is not the best approach to some problems. In this paper, we study an impact of the metric change on the SOM's performance in classification problems. In order to change the metric of the SOM we applied a distance metric learning method, so-called 'Large Margin Nearest Neighbour'. It computes the Mahalanobis matrix, which assures small distance between nearest neighbour points from the same class and separation of points belonging to different classes by large margin. Results are presented on several real data sets, containing for example recognition of written digits, spoken letters or faces.
YOUNG Star detrending for Transiting Exoplanet Recovery (YOUNGSTER) II: Using Self-Organising Maps to explore young star variability in Sectors 1-13 of TESS data
Dept. of Physics, University of Warwick（华威大学物理系）；Centre for Exoplanets and Habitability, University of Warwick（华威大学系外行星与宜居性中心）
A Feature-Major Codebook for Memory-Efficient Sparse-Binary Self-Organizing Maps: Scaling a MEDLINE Atlas to 1.05 Million Neurons on a Single Consumer GPU
College of Medicine and Dentistry, James Cook University（詹姆斯库克大学医学与牙科学院）
Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
Cloud to Street；Rutgers University（罗格斯大学）；European Space Agency（欧洲空间局）
为了解决气候脆弱地区洪水造成的日益严重的破坏，我们提出了Street to Cloud，这是一个机器学习管道，用于将众包的地面实况数据整合到洪水卫星图像的分割中。我们提出这种方法作为一种解决方案，以产生高质量的劳动密集型任务，手工标记的训练数据，并证明在我们的模型中不同的合理的众包方法的成功和失败。Street to Cloud利用社区报告和机器学习生成新颖的、接近实时的洪水范围洞察，用于应急响应。
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling
The Graduate Center of the City University of New York（纽约城市大学研究生中心）；Borough of Manhattan Community College - CUNY（纽约城市大学曼哈顿社区学院）；The City College of New York - CUNY（纽约城市大学纽约城市学院）
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records
Department of Electrical and Computer Engineering（电气与计算机工程系）；Duke University（杜克大学）；Department of Surgery（外科系）；Duke University School of Medicine（杜克大学医学院）；Department of Industrial and Systems Engineering（工业与系统工程系）；Georgia Institute of Technology（佐治亚理工学院）；Emory University School of Medicine（埃默里大学医学院）；Sloan School of Management（斯隆管理学院）；Massachusetts Institute of Technology（麻省理工学院）
Completion of partial structures using Patterson maps with the CrysFormer machine learning model
Department of Computer Science, Rice University（计算机科学系， Rice大学）；Rice University（Rice大学）；Computer Science Dept., Rice University（计算机科学系， Rice大学）；Ken Kennedy Institute, Rice University（Ken Kennedy研究所， Rice大学）；Department of BioSciences and Department of Chemistry, Rice University（生物科学系和化学系， Rice大学）
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
IBM T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Google Research（谷歌研究院）；Stanford University（斯坦福大学）；International Computer Science Institute（国际计算机科学研究所）；University of California at Berkeley（加州大学伯克利分校）
