◮ Evaluating the predictive performance of topic models is expensive and unreliable, despite recent advances in learning and inference algorithms. ◮ Annealed importance sampling (AIS), a Monte Carlo technique which operates by annealing between two distributions, has previously been successfully used for topic model evaluation. ◮ We introduce new AIS annealing paths which anneal from one topic model to another, thereby estimating the relative predictive performance of the models, and the progress of topic model training algorithms, efficiently and reliably.
['James R. Foulds', 'Padhraic Smyth']
Annealing Paths for the Evaluation of Topic Models