The elements of statistical learning. 2001

TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.

TL;DR: An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.

TL;DR: Focal loss as discussed by the authors focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training, which improves the accuracy of one-stage detectors.

TL;DR: Alternatives to the usual maximum likelihood estimates for the covariance matrices are proposed, characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk.

TL;DR: A penalized version of Fisher's linear discriminant analysis is described, designed for situations in which there are many highly correlated predictors, such as those obtained by discretizing a function, or the grey-scale values of the pixels in a series of images.

TL;DR: While VQ and the basic SOM are unsupervised clustering and learning methods, LVQ describes supervised learning, unlike in SOM, no neighborhoods around the “winner” are defined during learning in the basic LVQ, whereby also no spatial order of the codebook vectors is expected to ensue.

TL;DR: Boosting is one of the most powerful learning ideas introduced in the last ten years, but as will be seen in this chapter, it can profitably be extended to regression as well.