Popular GitHub Repositories for Circular Ensembles

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

Anomaly detection related books, papers, videos, and toolboxes

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Open-source framework for uncertainty and deep learning models in PyTorch :seedling:

Maxine VM: A meta-circular research VM

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

AI to Combat Environmental Pollution - detecting plastic waste in the environment to combat environmental pollution and promote circular economy (Deep Learning, PyTorch)

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

Snapshot Ensembles in Torch (Snapshot Ensembles: Train 1, Get M for Free)

InfiniteBoost: building infinite ensembles with gradient descent

Code for "Photometric Visual-Inertial Navigation with Uncertainty-Aware Ensembles" in TRO 2022

Supplementary material for IJCNN paper "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning"

Random Forests in Apache Spark

PyTorch implementation of Deep Randomized Ensembles for Metric Learning(ECCV2018)

Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan.

This repository provides the code used to create the results presented in "Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles".

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.

Parallel random matrix tools and complexity for deep learning