Yong Sun's repositories
ADRL
A Deep Reinforcement Learning Suite
AiLearning
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Building-Recommendation-Engines
Building Recommendation Engines by Packt
CoRide
Code for CIKM'19 "CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms"
Crowdsourced-Cleanup
Hackathon repository (1st place). Built using Python, Flask, JavaScript, jQuery and MongoDB
Deep-Reinforcement-Learning-Hands-On
Hands-on Deep Reinforcement Learning, published by Packt
ELSE-Efficient-and-Semantic-Location-Embedding
The code for "Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services" in IEEE BigData 2020
Federated-Learning-for-Human-Mobility-Models
Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility
GeoSAN
Code for KDD '20 paper "Geography-Aware Sequential Location Recommendation"
GraphEmbedding
Implementation and experiments of graph embedding algorithms.
graphsage-simple
Simple reference implementation of GraphSAGE.
Human-Mobility-Analysis
A toolkit and models for individual and crowd level human mobility analysis based on trajectory data, including travel destination prediction, travel spatiotemporal and semantic features calculations (e.g., entropy, radius of gyration, motif ratio, travel rhythm, etc), driving characters and dispositions.
knowledgeDistillation
PyTorch implementation of (Hinton) Knowledge Distillation and a base class for simple implementation of other distillation methods.
Mixture-of-Embedding-Experts
Mixture-of-Embeddings-Experts
netrand
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning / ICLR 2020
plan2vec
Public Release of Plan2vec Implementation in pyTorch
pygcn
Graph Convolutional Networks in PyTorch
pytorch_geometric
Geometric Deep Learning Extension Library for PyTorch
Regularized-GradientTD
Code repo for Gradient Temporal-Difference Learning with Regularized Corrections paper.
Reinforcement-Learning-An-Introduction-1
Codes and solutions to exercises from the book Introduction to Reinforcement Learning by Sutton and Barto
reinforcement-learning-an-introduction-2nd-edition
reinforcement learning
sari-tutorial
A SARI Tutorial for Mangrove Mapping
spatial-temporal-embedding
Attentive Traffic Flow Machines
t2vec
t2vec: Deep Representation Learning for Trajectory Similarity Computation
Urban-Informatics-and-Visualization-Berkeley-
Repo for class assignments and projects for UC Berkeley, CP255: Urban Informatics and Visualization
weiboanalysis
微博情感分析，文本分类，毕业设计项目
Word2Vec-with-side-information
基于side information版的 word2vec 《Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba》