From latent dynamics to meaningful representations

Understand

While representation learning has been central to the rise of machine learning and artificial intelligence, a key problem remains in making the learned representations meaningful.

• For this, the typical approach is to regularize the learned representation through prior probability distributions.
• However, such priors are usually unavailable or are ad hoc.
• To deal with this, recent efforts have shifted towards leveraging the insights from physical principles to guide the learning process.