Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to use...
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pi...
We introduce a generative model of dense flow fields within a layered representation of 3-dimensional scenes. Using probabilistic inference and learning techniques (namely, varia...
Nested-parallelism programming models, where the task graph associated to a computation is series-parallel, present good analysis properties that can be exploited for scheduling, c...
Title generation is a complex task involving both natural language understanding and natural language synthesis. In this paper, we propose a new probabilistic model for title gene...
Previous stochastic approaches to generation do not include a tree-based representation of syntax. While this may be adequate or even advantageous for some applications, other app...