A curated list of neural network pruning resources.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Papers for deep neural network compression and acceleration

MUSCO: MUlti-Stage COmpression of neural networks

Deep Learning Compression and Acceleration SDK -- deep model compression for Edge and IoT embedded systems, and deep model acceleration for clouds and private servers

IJCNN-19 and IEEE JSTSP-19 "Structured Pruning for Efficient ConvNets via Incremental Regularization"; BMVC-18 "Structured probabilistic pruning for convolutional neural network acceleration"

Reduce the model complexity by 612 times, and memory footprint by 19.5 times compared to base model, while achieving worst case accuracy threshold.