A Short Presentation on Probabilistic Programming and Variational Inference








Probabilistic Programming for Bayesian Computation: Part I

A presentation on variational inference and probabilistic programming.





Comments

Post a Comment

Popular posts from this blog

BWT for NLP (2)

Image
I show how the Burrows-Wheeler Transform can be used to compute the similarity between two strings. We submitted results from this method (along with results from the Context-Chain metric developed by my colleagues Frank Schilder and Ravi Kondadadi) for the Automatically Evaluating the Summaries of Peers (AESOP) task of the TAC 2009 conference. The task was to produce an automatic metric to evaluate machine generated summaries (i.e., system summaries) against human generated summaries for the TAC '09 Update Summarization Task. Clearly the automatic metric is just some function that produces a similarity score between the system summary and the human generated (the so-called model ) summary. The  proposed metrics were evaluated by comparing their rankings of the system summaries from different peers to that of the ranking produced by human judges. Similarity Metric We use an estimate of the conditional "compressibility" of the model summary given the system summary as the...
Read more

Incremental complexity support vector machine

Image
One of the problems with using complex kernels with support vector machines is that they tend to produce classification boundaries that are odd, like the ones below. (I generated them using a java SVM applet from here , whose reliability I cannot swear to, but have no reason to doubt.) Both SVM boundaries are with Gaussian RBF kernels: the first with $latex \sigma = 1$ and the second with $latex \sigma = 10$ on two different data sets. Note the segments of the boundary to the east of the blue examples in the bottom figure, and those to the south and to the north-east of the blue examples in the top figure. They seem to violate intuition. The reason for these anomalous boundaries is of course the large complexity of the function class induced by the RBF kernel with large $latex \sigma$, which gives the classifier a propensity to make subtle distinctions even in regions of  somewhat low example density. A possible solution: using complex kernels only where they are needed We propose to b...
Read more