Machine Learning & Data Analytics

We propose a new optimization algorithm that speeds up convergence using ideas from gauge theory in physics.

In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping.

We demonstrate the inference optimization capabilities of iterative inference models and show that they outperform standard inference models on several benchmark data sets of images and text.

We propose variance reduction by means of Quasi-Monte Carlo (QMC) sampling.

We present a novel approach to modeling stories using recurrent neural networks.

We have developed a system that can extract information from natural language stories, and allow for story-centric as well as character-centric reasoning.

In this paper, we propose to learn appearance measures for patches that are combined using deformable models.

In this paper, we tackle the largely overlooked problem of scheduling a multiplayer interactive narrative and propose the Live Interactive Narrative Scheduling Problem (LINSP), which handles reasoning under temporal uncertainty, resource scheduling, and non-linear plot choices.