wide-resnet-cifar100-kaggle

Fast and flexible AutoML with learning guarantees.

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

We present MocapNET, 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

Play deep learning with CIFAR datasets

NLP in Python with Deep Learning

Deep Learning ❤️ PyTorch

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

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