Awesome List Updates on Jan 21, 2025

9 awesome lists updated today.

1. Awesome Mongodb

skforecast (⭐1.3k) - Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.

Luis (⭐60) - LUIS (Love UI System) - A retained Mode UI Framework for LÖVE with Input processing (mouse, keyboard, touch, gamepad), Layer-, Grid-, State-, Theming-system, UI Editor, 16+ Widgets (for Desktop & Mobile).

Terrateam - GitOps-first open-source Infrastructure as Code automation for Terraform, OpenTofu, Terragrunt, CDKTF, and Pulumi.

Minimax entropy principle and its application to texture modeling - Neural Computing, 1997. [All Versions]. [Preprint]. This article proposes a general theory and methodology, called the minimax entropy principle, for building statistical models for images (or signals) in a variety of applications. This principle consists of two parts. The first is the maximum entropy principle for feature binding (or fusion): for a given set of observed feature statistics, a distribution can be built to bind these feature statistics together by maximizing the entropy over all distributions that reproduce them. The second part is the minimum entropy principle for feature selection: among all plausible sets of feature statistics, we choose the set whose maximum entropy distribution has the minimum entropy. Computational and inferential issues in both parts are addressed; in particular, a feature pursuit procedure is proposed for approximately selecting the optimal set of features. The minimax entropy principle is then corrected by considering the sample variation in the observed feature statistics, and an information criterion for feature pursuit is derived. The minimax entropy principle is applied to texture modeling, where a novel Markov random field (MRF) model, called FRAME (filter, random field, and minimax entropy), is derived, and encouraging results are obtained in experiments on a variety of texture images.

Parameter Expansion for Data Augmentation - Journal of the American Statistical Association, 1999. [All Versions]. [Preprint]. Viewing the observed data of a statistical model as incomplete and augmenting its missing parts are useful for clarifying concepts and central to the invention of two well-known statistical algorithms: expectation-maximization (EM) and data augmentation. Recently, the authors demonstrated that expanding the parameter space along with augmenting the missing data is useful for accelerating iterative computation in an EM algorithm. The main purpose of this article is to rigorously define a parameter expanded data augmentation (PX-DA) algorithm and to study its theoretical properties. The PX-DA is a special way of using auxiliary variables to accelerate Gibbs sampling algorithms and is closely related to reparameterization techniques.

m15a/nvim-srcerite (⭐5) - A colorscheme inspired by Srcery, based on nvim-highlite.

VAMM (Vinari OS Apache & MariaDB Manager) - Manages LAMP services using a GTK 3 GUI.

Population.NET (⭐37) - A .NET library allows clients to specify the exact fields they need, reducing unnecessary data transfer by avoiding the retrieval of all fields by default.

FastCloner (⭐148) - Fast deep cloning library for .NET 8+. Zero-config, works out of the box.