A Hierarchical Model for Ordinal Matrix Factorization

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. Importantly, these algorithms may be implemented in the factorization of very large matrices with missing entries. The model is evaluated on a collaborative filtering task, where users have rated a collection of movies and the system is asked to predict their ratings for other movies. The Netflix data set is used for evaluation, which consists of around 100 million ratings. Using root mean-squared error (RMSE) as an evaluation metric, results show that the suggested model outperforms alternative factorization techniques. Results also show how Gibbs sampling outperforms variational Bayes on this task, despite the large number of ratings and model parameters. Matlab implementations of the proposed algorithms are available from cogsys.imm.dtu.dk/ordinalmatrixfactorization.

Hierarchical approach to timing verification in CMOS VLSI design

This paper describes a novel hierarchical approach to timing verification. Four types of relationship existing among signal paths are distinguished, based on a classification of the degree of interdependency in the circuit. In this way, irrelevant path delays can be excluded through consideration of the interaction between critical paths and others. Furthermore, under suitable conditions, bounded delay values for large hierarchical systems can be deduced using bounded delays determined for their constituent cells. Finally, we discuss the impact on design strategy of the hierarchical delay model presented in this paper.