Posts
-
A Whole New Dimension of Optimization
Numerical Optimization: Optimizing multivariate objectives.
-
Take the Rough With the Smooth
Numerical Optimization: How helpful are smooth objectives? This post explores strategies for incorporating derivatives in optimization algorithms.
-
The Roots of No Evil
The first post in a planned series of posts about numerical optimization algorithms. This one is about extrema and roots.
-
Random Integers
Efficient sampling from a discrete distribution is a useful yet nontrivial algorithmic building-block, which involves some interesting and clever ideas.
-
Machine Learning. Literally.
Arguably, the most successful application of machine learning is largely unknown to most practitioners. Appropriately, it literally involves machines that learn.
-
Laws, Sausages and ConvNets
The nuts and bolts of Convolutional Neural Networks: algorithms, implementations and optimizations.
-
The Generative-Discriminative Fallacy
Machine learning algorithms are often categorized as either discriminative or generative. While this dichotomy can be instructive, it is often misleading.
-
Learning Dynamical Systems
Machine Learning meets Differential Equations.
-
The Name of The Rose
Convergent evolution is a common phenomenon in machine learning: many dissimilar scenarios lead to similar algorithms. When it comes to generalizations, though, distinctive underlying ideas could be fundamental.
-
Pointless Topology via Abstract Nonsense
Trigger Warning: pure mathematics. Stone Duality gives a rigorous meaning to the slogan “Geometry is dual to Algebra”.
-
Meta-Sequences in C++
With the introduction of variadic templates to C++, meta-sequences became a central idiom in meta-programming. The standard implementation is not always the best choice.
-
Random Bitstreams
Controlling the entropy of pseudo-random bits in Python when performance matters.
subscribe via RSS