Pinned Repositories
ppdiag
BotBuilder-Samples
Welcome to the Bot Framework samples repository. Here you will find task-focused samples in C#, JavaScript and TypeScript to help you get started with the Bot Framework SDK!
CityFlow
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
link-prediction
Representation learning for link prediction within social networks
magenta
Magenta: Music and Art Generation with Machine Intelligence
MTMSN
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
multidirectional-traffic-model
NavTL
network-eval
PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
sallyqiansun's Repositories
sallyqiansun/NavTL
sallyqiansun/BotBuilder-Samples
Welcome to the Bot Framework samples repository. Here you will find task-focused samples in C#, JavaScript and TypeScript to help you get started with the Bot Framework SDK!
sallyqiansun/CityFlow
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
sallyqiansun/link-prediction
Representation learning for link prediction within social networks
sallyqiansun/magenta
Magenta: Music and Art Generation with Machine Intelligence
sallyqiansun/MTMSN
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
sallyqiansun/multidirectional-traffic-model
sallyqiansun/network-eval
sallyqiansun/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
sallyqiansun/sumolights
SUMO adaptive traffic signal control - DQN, DDPG, Webster's, Max-pressure, Self-Organizing Traffic Lights
sallyqiansun/Traffic-state-reconstruction-using-Deep-CNN
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from timespace diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from NGSIM. Our results show that with probe vehicle penetration levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model’s reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation.
sallyqiansun/TSCC2019
Traffic Signal Control Competition
sallyqiansun/VRP-RL
Reinforcement Learning for Solving the Vehicle Routing Problem