zhaoyibo61 Goto Github PK

addernet

Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

air-quality-deep-learning-model

A CNN and LSTM approach to predicting air quality

air_pollution_forcast_beijing

基于Keras的LSTM多变量时间序列预测

airpollution-prediction

airpollutionnowcast

awesome-ml-summary

这是收集了很多的优秀的机器学习项目，将其做了整理

baidutraffic

This repo includes introduction, code and dataset of our paper Deep Sequence Learning with Auxiliary Information for Traffic Prediction (KDD 2018).

bcpf

Matlab code of Bayesian CP Factorization for Tensor Completion

bht-arima

Code for paper: Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting (AAAI-20)

botdad

Anomaly detection based on DNS traffic analysis

btd_caffe

Block Term Decomposition for Caffe model

caffe-tucker-decomposition

Caffe implementation of tucker tensor decomposition for convolutional layers

ccf-csp-and-pat-solution

CCF CSP和PAT考试题解（使用C++11语法）

cleanairproject_analysis

Analysis module for CleanAir Project, CNN & MLP to predict UFP (UltraFine Particles concentration) based on geospatial pollution measurements in a city

cnn_compression_rank_selection_bayesopt

Bayesian Optimization-Based Global Optimal Rank Selection for Compression of Convolutional Neural Networks, IEEE Access

cnn_model_optimizer

some ways to optimize CNN model

cnncompression

Reduce the computational complexity of a CNN by applying Tucker decomposition on pre-trained weights

codeofthesis

利用情感语义分析和张量数据结构预测股价

compress-neural-network

compressed-transformer

Compression of NMT transformer model with tensor methods

convolutional-neural-network-and-autoencoder-parallel

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery. An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction.

costnet

cp-decomposition

cp-tensor-completion

CP Tensor Completion for Spatial Temporal Data

cp-tpm-cnn

The CNN weight from "CP-decomposition with Tensor Power Method for Convolutional Neural Networks compression" (http://ieeexplore.ieee.org/document/7881725/)

cp-tpm-cnnv2

Continuation of CP-TPM-CNN.. Based on my master thesis https://www.researchgate.net/publication/319623885_Deep_Compression_of_Convolutional_Neural_Networks_with_Low-Rank_Approximation

decompose-cnn

CP and Tucker decomposition for Convolutional Neural Networks

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

deeplearningnotes

机器学习和量化分析学习进行中

deficient-efficient

Successfully training approximations to full-rank matrices for efficiency in deep learning.