ensemble

ML-Ensemble – high performance ensemble learning

Python package for stacking (machine learning technique)

NLP in Python with Deep Learning

Tabular Datasets ❤️ PyTorch

We present MocapNET2, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance (70 fps in CPU-only execution).

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Deep fusion project of deeply-fused nets, and the study on the connection to ensembling

Stacked Generalization (Ensemble Learning)

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

An implementation of Caruana et al's Ensemble Selection algorithm in Python, based on scikit-learn

numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法

ICDE'20 | A general & effective ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Deep Neural Network Ensembles for Time Series Classification

Video Face Manipulation Detection Through Ensemble of CNNs

[DEPRECATED] An innovative technique that constructs an ensemble of decision trees and converts this ensemble into a single, interpretable decision tree with an enhanced predictive performance

Simple sklearn based python implementation of Positive-Unlabeled (PU) classification using bagging based ensembles

A collection of codes for 'how far can we go with MNIST' challenge

Neutron: A pytorch based implementation of Transformer and its variants.

NeurIPS’20 | Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题

Time Series Ensemble Forecasting

Keras callback function for stochastic weight averaging

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

subsemble R package for ensemble learning on subsets of data

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

NLP学习笔记的Notebook，包含经典模型的理解与相关实践。