Popular GitHub Repositories for Stacked 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.

Stacked Generative Adversarial Networks

Tensorflow implementation of Stacked Hourglass Networks for Human Pose Estimation

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

Full pipeline for TianChi FashionAI clothes keypoints detection compitetion in TensorFlow

Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks

This repository contains PyTorch implementation of 4 different models for classification of emotions of the speech.

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

A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping

Implementation of "SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses"

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

基于 TensorFlow & PaddlePaddle 的通用序列标注算法库（目前包含 BiLSTM+CRF, Stacked-BiLSTM+CRF 和 IDCNN+CRF，更多算法正在持续添加中）实现中文分词（Tokenizer / segmentation）、词性标注（Part Of Speech, POS）和命名实体识别（Named Entity Recognition, NER）等序列标注任务。

Dataset and Code for our CVPR'18 paper ST-CGAN: "Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal"