Reading Notes

Reading notes

Introduction: My Reading Notes

Self-supervised Learning: Generative or Constrastive

Summary: Review of self-supervised learning.
Types: { paper }
Status:

Normalizing Flows: An Introduction and Review of Current Methods

Summary: To generate complicated distributions step by step from a simple and interpretable distribution.
Types: { paper }
Status:

The Grammar of Graphics

Summary: Reading notes for the book The Grammar of Graphics
Types: { book }
Status: 10%

Improving Document Ranking with Dual Word Embeddings

Summary: Word2vec produces two embedding spaces, the in-embedding and out-embedding.
Types: { paper }
Status:

The Art of Data Science

Summary: A nice and elegant book on data science
Types: { book }
Status:

Human Graphical Perception of Quantitative Information in Data Visualization

Summary: Data visualization caveats
Types: { paper }
Status:

Information Theory and Statistical Mechanics

Summary: Max entropy principle as a method to infer distributions of statistical systems
Types: { paper }
Status:

Schaum's Outline of Theories and Problems of Elements of Statistics I and II

Summary: The basics and all of modern statistics
References: - Schaum's Outline of Theories and Problems of Elements of Statistics I and II, by Ruth Bernstein and Stephen Bernstein
Types: { book }
Status: 80%

Data Mining: Concepts and Techniques

Summary: How data mining was done in the past
Types: { book }
Status: 70%

Overcoming catastrophic forgetting in neural networks

Summary: Using a newly defined loss function the authors could implement an idea that achieves the multi-task within one network.
Status:

Working Memory and Brain Waves

Summary: Working memory might be related to the background brain waves from theoretical point of view
Status:

Popularity versus similarity in growing networks

Summary: Introduce geometry into the manifold of complex networks
Status: