Important challenges for compiler optimization include determining what optimizations to apply, where to apply them and what is a good sequence in which to apply them. To address t...
In this paper, we develop multilingual supervised latent Dirichlet allocation (MLSLDA), a probabilistic generative model that allows insights gleaned from one language's data...
We extended language modeling approaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our approach is based on the anal...
This paper attacks a Japanese syllable-substitution cipher. We use a probabilistic, noisy-channel framework, exploiting various Japanese language models to drive the decipherment. ...
We present a computational framework designed to provide adaptive support aimed at triggering learning from problem-solving activities in the presence of worked-out examples. The k...