Topic: network-compression Goto Github
Some thing interesting about network-compression
Some thing interesting about network-compression
network-compression,Neural Network Quantization & Low-Bit Fixed Point Training For Hardware-Friendly Algorithm Design
User: a-suozhang
network-compression,Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons (AAAI 2019)
User: bhheo
network-compression,Knowledge Distillation with Adversarial Samples Supporting Decision Boundary (AAAI 2019)
User: bhheo
network-compression,Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)
Organization: cambridge-mlg
network-compression,Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" (ICCV 2019)
Organization: clovaai
network-compression,《李宏毅深度学习教程》(李宏毅老师推荐👍),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
Organization: datawhalechina
network-compression,Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
Organization: intellabs
network-compression,2020 INTERSPEECH, "Sparse Mixture of Local Experts for Efficient Speech Enhancement".
Organization: iu-saige
Home Page: https://saige.sice.indiana.edu/research-projects/sparse-mle/
network-compression,Group Fisher Pruning for Practical Network Compression(ICML2021)
User: jshilong
network-compression,Code implementation of our AISTATS'21 paper "Mirror Descent View for Neural Network Quantization"
User: kartikgupta-at-anu
network-compression,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 suggest a pruning strategy which is completely integrated in the training process and which requires only marginal extra computational cost. The method relies on unstructured weight pruning which is re-interpreted in a multiobjective learning approach. A batchwise Pruning strategy is selected to be compared using different optimization methods, of which one is a multiobjective optimization algorithm. As it takes over the choice of the weighting of the objective functions, it has a great advantage in terms of reducing the time consuming hyperparameter search each neural network training suffers from. Without any a priori training, post training, or parameter fine tuning we achieve highly reductions of the dense layers of two commonly used convolution neural networks (CNNs) resulting in only a marginal loss of performance. Our results empirically demonstrate that dense layers are overparameterized as with reducing up to 98 % of its edges they provide almost the same results. We contradict the theory that retraining after pruning neural networks is of great importance and opens new insights into the usage of multiobjective optimization techniques in machine learning algorithms in a Keras framework. The Stochastic Multi Gradient Descent Algorithm implementation in Python3 is for usage with Keras and adopted from paper of S. Liu and L. N. Vicente: "The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning". It is combined with weight pruning strategies to reduce network complexity and inference time.
User: malena1906
network-compression,
User: maxblumental
network-compression,Homework for Machine Learning (2019, Spring) at NTU
User: mortalhappiness
Home Page: http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML19.html
network-compression,MUSCO: MUlti-Stage COmpression of neural networks
Organization: musco-ai
network-compression,MUSCO: Multi-Stage COmpression of neural networks
Organization: musco-ai
network-compression,This is the official implementation of "DHP: Differentiable Meta Pruning via HyperNetworks".
User: ofsoundof
network-compression,Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression. CVPR2020.
User: ofsoundof
network-compression,Pytorch implemenation of "Learning Filter Basis for Convolutional Neural Network Compression" ICCV2019
User: ofsoundof
network-compression,:ring: Efficient tensor-based filter pruning
User: pvtien96
Home Page: https://github.com/pvtien96/CORING
network-compression,AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Organization: quic
Home Page: https://quic.github.io/aimet-pages/index.html
network-compression,AIMET GitHub pages documentation
Organization: quic
network-compression,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.
User: shaharpit809
network-compression,Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.
Organization: sony
Home Page: https://sony.github.io/model_optimization/
network-compression,Using ideas from product quantization for state-of-the-art neural network compression.
Organization: uber-research
network-compression,pruning
User: yifan122
network-compression,李宏毅教授 ML 2020 機器學習課程筆記 & 實作
User: yukaikw
network-compression,[NeurIPS 2021] Official PyTorch Code of Scaling Up Exact Neural Network Compression by ReLU Stability
User: yuxwind
network-compression,Deep Neural Network Compression based on Student-Teacher Network
User: zhengyu-li
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