The following are the posts:

$Equivariant$

Equivariant Neural Networks

How equivariance can be leveraged in neural network models

$Diffusion$

Diffusion Models and Ornstein-Uhlenbeck Processes

Exploring diffusion models with a closer look at the underlying random processes

$Learning$

Learning Efficiency under Manifold Hypothesis

How the underlining geometry of the data affects the difficulty of learning

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