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Thinking about High-Quality Human Data

[Special thank you to Ian Kivlichan for many useful pointers (E.g. the 100+ year old Nature paper “Vox populi”) and nice feedback. ]
High-quality data is the fuel for modern data deep learning model training. Most of task-specific labeled data comes from human annotation, such as classification task or RLHF labeling (which can be constructed as classification format) for LLM alignment training. Lots of ML techniques in the post can help with data quality, but fundamentally human data collection involves attention to details and careful execution. ....

United States , Aroyo Welty , Kohn Liang , Koh Liang , Mariya Toneva , Cohen Kappa Landis Koch , Chris Callison Burch , Neural Network Learning , A Survey Of Quality , Amazon Mechanical Turk , Machine Translation , Graph Modeling , Multi Annotator Competence Estimation , Variational Bayes , Gab Hate Corpus , Noisy Cross Validation , Iterative Noisy Cross Validation , Data Cascades , Evaluating Translation Quality Using Amazon , Contrasting Data Annotation Paradigms , Crowd Truth , Seven Myths , Agrees Is Not Gold , Evaluating Ground Truth Labels , Dialogue Content , Rater Disagreements ,

"Deep Compositional Spatial Models" by Andrew Zammit-Mangion, Tin Lok James Ng et al.

Abstract
Spatial processes with nonstationary and anisotropic covariance structure are often used when modeling, analyzing, and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping function is generally difficult to fit and not constrained to be injective, often resulting in “space-folding.” Here, we propose modeling an injective warping function through a composition of multiple elemental injective functions in a deep-learning framework. We consider two cases; first, when these functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. Inspired by recent methodological and technological advances in deep learning and deep Gaussian processes, we employ approximate Bayesian methods to make inference with these models using graphics processing units. Through simulation studi ....

Deep Models , Spatial Statistics , Stochastic Processes , Variational Bayes , ஆழமான மாதிரிகள் ,