network-compression

Deep Compression on AlexNet

🚀 Coupled Tensor Decomposition for Compact Network Representation

Spectral and randomized graph sparsification reducing dense graph edges while preserving approximate cut and connectivity properties.

pruning

Code implementation of our AISTATS'21 paper "Mirror Descent View for Neural Network Quantization"

Deep autoencoder-based neural network embedding framework for functional microconnectome analysis and network-metric interpretation.

[NeurIPS 2021] Official PyTorch Code of Scaling Up Exact Neural Network Compression by ReLU Stability

ACM SAC 2025 Accepted Paper

collection of works aiming at reducing model sizes or the ASIC/FPGA accelerator for machine learning

Spectral and randomized graph sparsification reducing dense graph edges while preserving approximate cut and connectivity properties.

Fold your MacBook display for a soft, blurring desktop effect—animated live at 60 fps with lid-angle smoothing on Apple Silicon.

用 Zstandard 替代原版 zlib 压缩 Minecraft 网络流量（Velocity / Paper / Fabric 三端）

2020 INTERSPEECH, "Sparse Mixture of Local Experts for Efficient Speech Enhancement".

MUSCO: Multi-Stage COmpression of neural networks

This repository consists of application of Deep Learning Models like DNN, CNN (1D and 2D), RNN (LSTM and GRU) and Variational Autoencoders written from scratch in tensorflow.

Overparameterization and overfitting are common concerns when designing and training deep neural networks. Network pruning is an effective strategy used to reduce or limit the network complexity, but often suffers from time and computational intensive procedures to identify the most important connections and best performing hyperparameters. We s…

Deep Neural Network Compression based on Student-Teacher Network

Homework for Machine Learning (2019, Spring) at NTU

李宏毅教授 ML 2020 機器學習課程筆記 & 實作