leilin-research

aas

Code to accompany Advanced Analytics with Spark from O'Reilly Media

AE_ts

Auto encoder for time series

AGPPVAE

Adversarial Gaussian Process Prior Variational Autoencoder

Automatic-Parking

Automatic parking with Reinforcement Learning (Q learning with epsilon greedy algorithm) in simulation. A parking environment is created in both Matplotlib and Gazebo.

GCGRNN

Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF)

machine-learning-notes-1

机器学习笔记

medium_posts

traffic_uncertainty

VTP

Vehicle Trajectory Prediction with Deep Learning Models

leilin-research/VTP

Vehicle Trajectory Prediction with Deep Learning Models

leilin-research/GCGRNN

Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF)

leilin-research/machine-learning-notes-1

机器学习笔记

leilin-research/medium_posts

leilin-research/AGPPVAE

Adversarial Gaussian Process Prior Variational Autoencoder

leilin-research/awesome-public-datasets

A topic-centric list of HQ open datasets in public domains. PR ☛☛☛

leilin-research/bordervolumedownload

The C# crawler to download the border volume http://www.peacebridge.com/index.php/historical-traffic-statistics/daily-volumes

leilin-research/cqr

Conformalized Quantile Regression

leilin-research/drsa

Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling.

leilin-research/examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

leilin-research/Facial-Similarity-with-Siamese-Networks-in-Pytorch

Implementing Siamese networks with a contrastive loss for similarity learning

leilin-research/gconvRNN

Graph convolutional recurrent neural network

leilin-research/GetOldTweets-python

A project written in Python to get old tweets, it bypass some limitations of Twitter Official API.

leilin-research/helit

My machine learning/computer vision library for all of my recent papers, plus algorithms that I just like.

leilin-research/machine_learning_miniprojects

leilin-research/manning

repo for Manning book Deep Learning with Structured Data

leilin-research/markdown-cheatsheet

Markdown Cheatsheet for Github Readme.md

leilin-research/miscellaneous

leilin-research/MultitaskAIS

A multi-task model for vessel monitoring using AIS data streams

leilin-research/notes-on-dirichlet-processes

:game_die: Notes explaining Dirichlet Processes, HDPs, and Latent Dirichlet Allocation

leilin-research/pytorch-seq2seq

An open source framework for seq2seq models in PyTorch.

leilin-research/pytorch-seq2seq-1

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. [IN PROGRESS]

leilin-research/Q-Learning-based-smart-cab

Problem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:  On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.  On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.

leilin-research/reinforcement_learning_course_materials

Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University

leilin-research/reuters

leilin-research/SageMaker-Sentiment-Classification-Twitter-Stock-Data

use BERT

leilin-research/system-design

High-level system design and object-oriented design

leilin-research/Text-Classification-Pytorch

Text classification using deep learning models in Pytorch

leilin-research/Time-series-prediction

A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats

leilin-research/xiangrongwang.github.io