Mathematics
The theoretical underpinning of machine learning and statistics.
Math is the underpinning of machine learning and statistics.
The accuracy of a model depends on the mathematical assumptions we instill. The quality of an algorithm is the distance between its prediction and the truth, as so defined by mathematics. We can even estimate the amount by which we'll be wrong.
This is more than just numbers and equations.
We ask: what is the geometry of an ideal and accurate model? What does it mean that a neural network linearly transforms dimensions to make its predictions? What does the world look like in the space where these algorithms do their thinking?
Sometimes the answers are surprising, and sometimes they are not. But they are always interesting.
Check out the papers we're reading.
- Deep Learning Interpretation via ManifoldsZhang et al.
Deep learning algorithms are notoriously difficult to interpret. Mathematicians give it a shot anyway.
optimizationgeometry - Manifolds: an Information-Theoretic ApproachChigirev et al.
Geometric methods for optimization with structure.
optimizationgeometry - Manifold Learning via Mixture ModelsAlberto et al.
Geometric methods for optimization with structure.
optimizationgeometry