There are 0 repository under lasso topic.
an SSO and OAuth / OIDC login solution for Nginx using the auth_request module
A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.
Automatically sets the affinity of "audiodg.exe" to only one core on computer start to fix the crackling noises that can occur in VoiceMeeter.
Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
Lasso/Elastic Net linear and generalized linear models
MIRT: Michigan Image Reconstruction Toolbox (Julia version)
Functional models and algorithms for sparse signal processing
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers https://arxiv.org/abs/1802.00124
This is implementation Lasso with Coordinate Descent and LARS (Least Angle Regression).
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
L1-regularized least squares with PyTorch
Matlab library for gradient descent algorithms: Version 1.0.1
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
Image inpainting via dictionary learning and sparse representation.
A simulation framework for topology identification and model parameter estimation in power distribution grids: https://ieeexplore.ieee.org/document/8601410
Quantified Sleep: Machine learning techniques for observational n-of-1 studies.
Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm
A free and open source vector drawing tool for mobile.
Strapi CMS plugin that provides point list field for selecting areas on images
Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.