Hi all!
So I have just released a new variational Bayes topic modeling package for Julia, which can be found here: https://github.com/esproff/TopicModelsVB.jl The models included are: 1. Latent Dirichlet Allocation (LDA) 2. Filtered Latent Dirichlet Allocation (fLDA) 3. Correlated Topic Model (CTM) 4. Filtered Correlated Topic Model (fCTM) 5. Dynamic Topic Model (DTM) 6. Collaborative Topic Poisson Factorization (CTPF) This is, as far as I can tell, the best open-source topic modeling package to date. It's still a bit rough around the edges and there are a few edge-case bugs I think still deep in the belly of 1 or 2 of the algorithms. But overall it's polished enough that I think it needs to be tried out by other people besides myself. I'm open to collaborators, and I'm especially interested in adding some GPGPU support, however, formally speaking, I'm trained as a mathematician, not a computer scientist or software engineer, and thus if you're an expert in GPGPU I'd be very interested in talking to you about adding this functionality as Bayesian learning can be *EXTREMELY *computationally intensive. (you can contact me on here or at [email protected]) On the other hand, if you're more into the applied math / machine learning side, there are still a number of models to implement, mostly non-parametric versions of the ones I've implemented, however I should warn you that Bayesian nonparametrics is not for the faint of heart. Julia is a great language, and I hope you all like it as much as I do, of course the speed is the big seller, however I think maybe its best feature is the ease with which one can dig down into the internals of the language, and considering how high-level the language is, this is truly a masterstroke by the creators.
