It is wasteful to store zeros elements in a sparse matrix, especially for incrementally data. When constructing tf-idf and bag-of-words features or saving graph ajacent matrix, non-efficient sparse matrix storage might lead to the memory error. To circumvent this problems, efficient sparse matrix storage is a choice.

Notes on clustering approaches.

Graph Neural Networks (GNNs) has demonstrated efficacy on non-Euclidean data, such as social media, bioinformatics, etc.

GANs are widely applied to estimate generative models without any explicit density function, which instead take the game-theoretic approach: learn to generate from training distribution via 2-player games.

This is a concise introduction of Variational Autoencoder (VAE).

When we check and filter out the duplicates for a web crawler, bloom filter is a good choice to curtail the memory cost. Here is a brief introduction.

Flow models are used to learn continuous data.

The brain has about 10¹⁴ synapses and we only live for about 10⁹ seconds. So we have a lot more parameters than data. This motivates the idea that we must do a lot of unsupervised learning since the perceptual input (including proprioception) is the only place we can get 10⁵ dimensions of constraint per second.
(Geoffrey Hinton)

Techniques of NN training. Keep updating.

This is an introduction of recent BERT families.