model-compression topic

Train neural networks with joint quantization and pruning on both weights and activations using any pytorch modules

mlzxy

[Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning

MingSun-Tse

[ICML 2022] "DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks", by Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman K...

GATECH-EIC

[ACL 2023] Code for paper “Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation”(https://arxiv.org/abs/2305.09651)

twinkle0331

[NeurIPS'22] What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective

MingSun-Tse

Learnable Embedding Space for Efficient Neural Architecture Compression

Shengcao-Cao

This repository is the official implementation of the paper Pruning via Iterative Ranking of Sensitivity Statistics and implements novel pruning / compression algorithms for deep learning / neural net...

StijnVerdenius

Resources of our survey paper "A Systematic Review of AI Deployment on Resource-Constrained Edge Devices: Challenges, Techniques, and Applications"

wangxb96

[ICLR'23] Trainability Preserving Neural Pruning (PyTorch)

MingSun-Tse

Code for CHIP: CHannel Independence-based Pruning for Compact Neural Networks (NeruIPS 2021).

Eclipsess