The pdf contains some notes on several papers, some of which only have 1-2 slide pages. Again, red fonts are my thoughts and insights. I wish my notes can help readers to better understand the new concepts and get inspired.
Click to view:
Continue reading “Slideshare (8): Graph Neural Network Paper reading notes”
During this summer, I did a project on cross-lingual NLP tasks. Recently I was working my notes and I organized them into a better format. I would like to share some of the notes with the readers who might be interested in this topic.
Papers covered:
A quick introduction to Graph Neural Networks:
List of papers covered:
Semi-Supervised Classification with Graph Convolutional Networks
Graph Attention Networks
Variational Graph Auto-Encoders
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
For the past few years after my Master’s, I did many jobs, long term, short term, internship, or full-time. I also had too many interviews, some of them I failed. Together with my friends, we had collected many materials, including basic algorithms, popular questions, basic machine learning knowledge, and deep learning knowledge. Then I organized them as one huge PDF (150+ pages).
A very brief outline:
The material covers some screenshots from other people’s lectures and books. [Some slide pages are not in English! I am too lazy to translate them..]
I went through this PDF each time before there is an interview, in the case to answer questions like “what is knn”. I hope you may find the material useful. Download link:
Recently, I am working on a new version by adding more deep learning basics.
New items need to be updated: Merge sort; Sorting code in Python; Boyer-Moore Vote Algorithm.
Please check my notes for Transfer Learning introduction!
A brief introduction on unsupervised transfer learning methods.
The presentation focused on unsupervised transfer learning methods, introducing feature-based and model-based strategies and few recent papers from ICML, ACL.
Unsupervised Transfer Learning
Comments are welcomed!
