Keith Adams

#TagSpace: Semantic Embeddings from Hashtags

We describe a convolutional neural network that learns feature representations for short textual posts using hashtags as a supervised signal. The proposed approach is trained on up to 5.5 billion words predicting 100,000 possible hashtags. As well as strong performance on the hashtag prediction task itself, we show that its learned representation of text (ignoring the hashtag labels) is useful for other tasks as well. To that end, we present results on a document recommendation task, where it also outperforms a number of baselines.

The hiphop virtual machine

The HipHop Virtual Machine (HHVM) is a JIT compiler and runtime for PHP. While PHP values are dynamically typed, real programs often have latent types that are useful for optimization once discovered. Some types can be proven through static analysis, but limitations in the ahead-of-time approach leave some types to be discovered at run time. And even though many values have latent types, PHP programs can also contain polymorphic variables and expressions, which must be handled without catastrophic slowdown. HHVM discovers latent types by structuring its JIT around the concept of a tracelet. A tracelet is approximately a basic block specialized for a particular set of run-time types for its input values. Tracelets allow HHVM to exactly and efficiently learn the types observed by the program, while using a simple compiler. This paper shows that this approach enables HHVM to achieve high levels of performance, without sacrificing compatibility or interactivity.