We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation c...
With the growing importance of model-driven development, the ability of transforming models into well-defined semantic domains becomes a key to automated code generation or verifi...
Modern software is increasingly concurrent, timed, distributed, and therefore, non-deterministic. While it is well known that tests can be generated as LTL or CTL model checker co...
Topological representations are being used to define geometric models suitable for grid generation and grid generation tools are being developed that work directly on topological ...
Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to i...