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Reading memo for GRL
* Book: GRL (Graph Representation Learning) by W.L. Hamilton (2020)
General points for thorough reading (not extensive reading)
- Read every words and mark/memorize important words.
- Try to understand every piece the authors want to tell us - if needed, read other reference(s).
- However, if you cannot understand something after you have tried more than 30 minutes, discuss it with others.
- Try to use low-entropy description in explaining.
#01, liangz
range: from the title page to p8 (end of Chapter 1)
i: title page
- official book
- author William L. Hamilton was an Assistant Professor at that time at McGill Univ.
- McGill Univ., a famous university in Canada, 12 Nobel laureates, and graduate Yoshua Bengio (2018 ACM Turing Award, one of the three Godfathers of deep learning)
ii: Abstract page
- no-free-lunch theorem
- ⇒ inductive bias is important before optimization (and before machine learning). Ex. Consider to use a linear regression to analyze a dataset. The assumption that a linear regression is approporiate is an inductive bias.
- What is induction?
- Lin & Tegmark'16 Why does deep and cheap learning work so well? ⇒ Assumptions: (1) Low polynomial order in the real world (2) Locality of data (3) Symmetry phenomenon.
- 3 graph learning topics: (1) embedding (2) CNN → Graph (3) Message-passing approach
iii-v: Contents
- not important now
vi: Preface
- “past seven years” ⇒ graph learning started from 2013
- at 2020, fastest growing sub-areas of deep learning
- audience: shall have some background in machine learning and deep learning (e.g., Goodfellow et al, 2016)
vii-viii: Acknowledgements
- connections of the author (e.g., Jure Leskovec is a famous researcher in network science)
p1: Chapter 1 Introduction
- node, edge, graph ⇒ ask the audience to illustrate some graphs
- Zachary Karate Club Network (1977)
public/grl_readingmemo.1712211047.txt.gz · Last modified: 2024/04/04 15:10 by liang