In The Elements of Statistical Learning (ESL), a benchmark of “Off-the-Shelf” Procedures for Data Mining is provided p371. They provide a benchmark comparing Neural Nets, SVM, Trees, MARS, KNN with kernels, in terms of handling of mixed data types, missing values, robustness (to outliers, to irrelevant inputs, to monotone transformation of inputs), ability to extract linear dependence, computational scalability, interpretability and predictive power. Another model benchmark is given by Top 10 algorithms in data mining, which provide an introduction to: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Those benchmarks date respectively from 2008 and 2007, while new techniques appeared since then. I am mostly thinking about recent papers : Residual Networks (2015), Capsules (2017), Neural Ordinary Differential Equations (2018).
Are there up to date benchmarks for more recent “Off-the-Shelf” procedures for Data Mining ? Or, alternatively, can we consider those models as subparts of Neural networks ? Not recent enough to be considered "Off the shelf", thus benchmarked ?