Deep Compression on AlexNet

ACM SAC 2025 Submitted Paper

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

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.

Homework for Machine Learning (2019, Spring) at NTU

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

🧠 Singular values-driven automated filter pruning

pruning

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

AIMET GitHub pages documentation

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

MUSCO: Multi-Stage COmpression of neural networks

Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)

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…

💍 Efficient tensor decomposition-based filter pruning

李宏毅教授 ML 2020 機器學習課程筆記 & 實作

Deep Neural Network Compression based on Student-Teacher Network

Pytorch implemenation of "Learning Filter Basis for Convolutional Neural Network Compression" ICCV2019

This is the official implementation of "DHP: Differentiable Meta Pruning via HyperNetworks".