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age-and-gender

同时识别年龄与性别

art

atics_i

First module assignment for Advanced Topics In Computer Science course held at the "Roma Tre" University of Rome.

case

code for Truth Discovery by Claim and Source Embedding

covid-19-visualization

武汉2019新型冠状病毒疫情可视化(ncov全国疫情地图及时间轴变化，各省市地图及疫情曲线)，疫情数据分析系统，疫情小区可视化，WuHan 2019-nCoV Data Visualization Analysis System (前端+后端+数据清洗)

deep-learning-with-keras-notebooks

Jupyter notebooks for using & learning Keras

deeplearningtutorials-1

Deep Learning与PyTorch入门实战视频教程

eatnn

This is our implementation of EATNN: Efficient Adaptive Transfer Neural Network (SIGIR 2019)

epidemic1_big_screen

excelhandle

This is a useful tool.

fair-truth-discovery

fchd-fully-convolutional-head-detector

Code for FCHD - A fast and accurate head detector

girls-in-ai

免费学代码系列：小白python入门、数据分析data analyst、机器学习machine learning、深度学习deep learning、kaggle实战

graph-based-recommendation-system

building a recommendation system using graph search methodologies. We will be comparing these different approaches and closely observe the limitations of each.

graph-based-recommendation-system-1

Graph convolutional matrix completion

graphneuralnetwork

《深入浅出图神经网络：GNN原理解析》配套代码

graphrec-www19

Graph Neural Networks for Social Recommendation, WWW'19

graphrec_pytorch

A PyTorch implementation of Graph Neural Networks for Social Recommendation (GraphRec)

hugbert

Hugging BERT together. Misc scripts for Huggingface transformers.

incentive-mechanism-design-for-truth-discovery-in-crowdsourcing-with-copiers

kdem

This repository includes data and code for the algorithm of Kernel Density Estimation from Multiple Sources (KDEm) proposed in a KDD'16 paper

keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

learnopencv

Learn OpenCV : C++ and Python Examples

nannan

optimal-filtering-rating

population---truth-discovery

projettd-ac

This paper presents TD-AC which is an effective algorithm for the truth discovery problem when the attributes over data are structurally correlated. We build our procedure on an abstract representation of the truth in the data, the k-means clustering technique and the silhouette measure to automatically find an optimal partitioning of the input data (or a near-optimal) maximizing the accuracy of any base truth discovery process. The intensive experiments conducted on synthetic and real datasets show that TD-AC outperforms existing partitioning approaches with a more reasonable running time. It improves on synthetic datasets the accuracy of standard truth discovery algorithms by 6% at least and by 16% at most and also significantly when the data coverage rate is high for the other types of datasets

pseudo_labeling_small_datasets

Pseudo Labeling for Neural Networks and Logistic Regression/SVMs ( Based on "Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks")

python-machine-learning

机器学习算法项目

pytorch_geometric

Geometric Deep Learning Extension Library for PyTorch