Echo State Networks (ESNs) are known for their fast and precise one-shot learning of time series. But they often need good hyper-parameter tuning for best performance. For this good validation is key, but usually, a single validation split is used. In this rather practical contribution we suggest several schemes for cross-validating ESNs and introduce an efficient algorithm for implementing them. The component that dominates the time complexity of the already quite fast ESN training remains constant (does not scale up with $k$) in our proposed method of doing $k$-fold cross-validation. The component that does scale linearly with $k$ starts dominating only in some not very common situations. Thus in many situations $k$-fold cross-validation of ESNs can be done for virtually the same time complexity as a simple single split validation. Space complexity can also remain the same. We also discuss when the proposed validation schemes for ESNs could be beneficial and empirically investigate them on several different real-world datasets.
Nested cross-validation when selecting classifiers is overzealous for most practical applications
University of Campinas（坎皮纳斯大学）；University of East Anglia（东英吉利大学）
当选择要应用于特定问题的分类算法时，必须同时选择该数据集的最佳算法\emph{and}和所选模型的最佳超参数集。通常的方法是应用嵌套交叉验证过程;在内部交叉验证中执行超参数选择，而外部交叉验证计算算法\emph{with cross-validation based hyperparameter tuning}的预期准确度的无偏估计。另一种方法，我们称之为“平面交叉验证”，使用一个单一的交叉验证步骤来选择最佳超参数值，并提供算法的预期准确度的估计，尽管有偏差，但仍然可以用于选择最佳学习算法。我们使用12种不同的算法对115个真实生活中的二进制数据集进行了测试，并得出结论，使用计算成本较低的平面交叉验证程序通常会导致选择一种算法，该算法对于所有实际目的而言，与通过嵌套交叉验证选择的算法具有相似的质量，前提是学习算法具有相对较少的超参数进行优化。
Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection
School of Computer Science and Software Engineering, Southwest Petroleum University（西南石油大学计算机科学与软件工程学院）；School of Electronic Information and Artificial Intelligence, Leshan Normal University（乐山师范学院电子信息与人工智能学院）
Bootstrap Bias Corrected Cross Validation applied to Super Learning
Computational Centre, University of Bialystok（比亚韦斯托克大学计算中心）；Institute of Informatics, University of Bialystok（比亚韦斯托克大学信息学院）；Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw（华沙大学跨学科数学与计算建模中心）
FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Guiyang Academy of Information Technology（贵阳信息技术研究院）；NTT DATA Corporation（NTT DATA株式会社）；NTT DATA Institute of Management Consulting Inc.（NTT DATA经营研究所）
Department of Analytics, Marketing and Operations, Imperial Business School, UK（分析、营销与运营系，帝国商业学院，英国）；Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, CA, USA（工业与系统工程系，维特里商学院，南加州大学，美国）
Novel split quality measures for stratified multilabel Cross Validation with application to large and sparse gene ontology datasets
Institute of Biotechnology（生物技术研究所）；Helsinki Institute of Life Sciences (HiLife)（赫尔辛基生命科学研究所（HiLife））；University of Helsinki（赫尔辛基大学）；Faculty of Biosciences（生物科学学院）
VISION-ICE: Video-based Interpretation and Spatial Identification of Arrhythmia Origins via Neural Networks in Intracardiac Echocardiography
Department of Electrical and Computer Engineering, Rice University, United States of America（电气与计算机工程系，里士大学，美国）；Electrophysiology Clinical Research and Innovations, Texas Heart Institute, United States of America（电生理临床研究与创新，德克萨斯心脏研究所，美国）；Department of Cardiology, Texas Heart Institute, United States of America（心内科部，德克萨斯心脏研究所，美国）
Efficient Cross-Validation for Sparse Linear Regression
Department of Analytics, Marketing and Operations, Imperial Business School, London, UK（分析、营销与运营系，帝国商务学院，伦敦，英国）；Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, CA（工业与系统工程系，维特比工程学院，美国南加州大学，CA）
