Machine Learning, Dynamical Systems, and All That

Personal Take

Deep learning is some kind of optimal control problem (with the control parameters optimized, for a proper objective, using gradient descent based algorithms and some randomization tricks) for randomly initialized open dynamical systems (deep architectures) interacting with a noisy environment (large amount of typically noisy data), with the hope that the solution found can be applied successfully to new environments (test data, possibly poor-quality).

Selected Papers

Modern ML x Must-Read:

Sequence Modeling:

Neural Differential Equations and All That:

Understanding Modern ML + DS:

Using ML to Study DS:

Model Robustness:

Generative Modeling:

Complex Networks x Dynamical Systems:

Other Noteworthy Papers: