The initial intent to write this blog is to sorting out the paper I read in 2018. As the AI area is changing and new techs are coming up every minute, so I want myself to keep up to date of the remarkable papers. Stay hungry, stay foolish.

The papers mainly can be divided in several eras: utils of Data science, Machine Learning, Reinforcement Learning, Computer Vision, deep learning. And each can be divided into more specific era. If you are a recruiter, happy to be asked about them or discuss about them.

Deep Learning

Utils of DL

  • Understanding the difficulty of training deep feedforward neural networks
  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
  • Deep Residual Learning for Image Recognition

RNN

  • Visualizing and Understanding Recurrent Networks

Reinforcement Learning

  • Policy Gradient Methods for Reinforcement Learning with Function Approximation
  • Trust Region Policy Optimization
  • Asynchronous Methods for Deep Reinforcement Learning
  • Proximal Policy Optimization Algorithms

MCTS

  • Mastering the game of Go with deep nerual networks and tree search
  • Mastering the game of Go without human knowledge

Machine Learning

Utils of ML

  • Exploratory Understanding for Class-Imbalance Learning
  • SMOTE: Synthetic Minority Over-sampling Technique
  • Leakage in Data Mining: Formula, Detection, and Avoidance
  • Term Paper: How and Why to Use stochastic Gradient Descent?

TreeS and tree based

  • lightGBM: A Highly Efficient Gradient Boosting Decision Tree
  • XGBoost: A Scalable Tree Boosting System

SVM

  • A Practical Guide to Support Vector Classification

Clustering

  • A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

Project Reference Papers

  • Deep Learning for Identifying Breast Cancer
  • Resource Management with Deep Reinforcement Learning
  • Customized Regression Model for Airbnb Dynamic Pricing

todo

  • Relational inductive biases, deep learning, and graph networks