Reading Notes

Reading notes

Introduction: My Reading Notes

MTGNN

Summary: Key Components Time Convolution (TC) Module Time Convolution The temporal convolution is responsible for capturing temporal patterns in a sequence. Graph Convolution Module Mix-hop Propagation in GNN Mix-hop is a strategy to avoid oversmoothing in GNN Graph Structure Learning Layer Graph Structure Learning in GNN We can learn a graph structure without prior knowledge Architecture Wu et al., 2020
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StemGNN

Summary: What problem is StemGNN solving: intra-series temporal pattern: DFT Each series inter-series correlations At each step, the interactions between nodes reversible operator Example problem Covid cases: DE, AT, NL, … Predicting each country without considering the interactions between them Or introduce the people flow between them GFT: Completes DFT as it takes care of the inter-series correlations one extra slide for this topic Convolutions on Graphs one extra slide for this topic Graph Basics How to build the graph “self-attention”: outer product of key, query, as the adjacency matrix key, query are of length # of ndoes Weights and Biases LC 1DConv GLU FC Experiments Traffic adjacency matrix neighbouring sensors have higher correlations Covid Neighbouring countries have higher correlation Spetral analysis: Some eigenvectors have clear meanings Week spots Paper and code are not consistent https://github.
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Self-supervised Learning: Generative or Constrastive

Summary: Review of self-supervised learning.
Types: { paper }
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Normalizing Flows: An Introduction and Review of Current Methods

Summary: To generate complicated distributions step by step from a simple and interpretable distribution.
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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 }
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The Art of Data Science

Summary: A nice and elegant book on data science
Types: { book }
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Human Graphical Perception of Quantitative Information in Data Visualization

Summary: Data visualization caveats
Types: { paper }
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Information Theory and Statistical Mechanics

Summary: Max entropy principle as a method to infer distributions of statistical systems
Types: { paper }
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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.
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Working Memory and Brain Waves

Summary: Working memory might be related to the background brain waves from theoretical point of view
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Popularity versus similarity in growing networks

Summary: Introduce geometry into the manifold of complex networks
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