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Tag Archives: machine learning
You deserve better than twosided finite differences
In calc 101, the derivative is derived as . So, if you want to estimate a derivative, an easy way to do so would be to just pick some small and estimate: This can work OK. Let’s look at an … Continue reading
Sneaking up on Bayesian Inference (A fable in four acts)
Act 1: Magical Monkeys Two monkeys, Alfred () and Betty () live in a parallel universe with two kinds of blocks, green () and yellow (). Alfred likes green blocks, and Betty prefers the yellow blocks. One day, a Wizard … Continue reading
Favorite things NIPS
I always enjoy reading conference reports, so I thought I’d mention a few papers that caught my eye. (I welcome any corrections to my summaries of any of these.) 1. Recent Progress in the Structure of LargeTreewidth Graphs and Some … Continue reading
Truncated BiLevel Optimization
In 2012, I wrote a paper that I probably should have called “truncated bilevel optimization”. I vaguely remembered telling the reviewers I would release some code, so I’m finally getting around to it. The idea of bilevel optimization is quite … Continue reading
Posted in Uncategorized
Tagged crossvalidation, machine learning, matlab, optimization, regularization
5 Comments
Reducing Sigmoid computations by (at least) 88.0797077977882%
A classic implementation issue in machine learning is reducing the cost of computing the sigmoid function . Specifically, it is common to profile your code and discover that 90% of the time is spent computing the in that function. This … Continue reading
Posted in Uncategorized
Tagged boltzmann machines, efficiency, machine learning, math, neural networks
9 Comments
CRF Toolbox Updated
I updated the code for my Graphical Models / Conditional Random Fields toolbox This is a Matlab toolbox, though almost all the real work is done in compiled C++ for efficiency. The main improvements are: Lots of bugfixes. Various small … Continue reading
Personal opinions about graphical models 1: The surrogate likelihood exists and you should use it.
When talking about graphical models with people (particularly computer vision folks) I find myself advancing a few opinions over and over again. So, in an effort to stop bothering people at conferences, I thought I’d write a few entries here. … Continue reading
Posted in Uncategorized
Tagged graphical models, machine learning, probabilistic inference
7 Comments