Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A constraint extension to scalable vector graphics
Proceedings of the 10th international conference on World Wide Web
The Cassowary linear arithmetic constraint solving algorithm
ACM Transactions on Computer-Human Interaction (TOCHI)
Lessons learned about one-way, dataflow constraints in the Garnet and Amulet graphical toolkits
ACM Transactions on Programming Languages and Systems (TOPLAS)
On the pagination of complex documents
Computer Science in Perspective
Adaptive grid-based document layout
ACM SIGGRAPH 2003 Papers
Optimal pagination techniques for automatic typesetting systems
Optimal pagination techniques for automatic typesetting systems
The Journal of Machine Learning Research
A framework for structure, layout & function in documents
Proceedings of the 2005 ACM symposium on Document engineering
Adaptive layout for dynamically aggregated documents
Proceedings of the 13th international conference on Intelligent user interfaces
Review of automatic document formatting
Proceedings of the 9th ACM symposium on Document engineering
Active layout engine: Algorithms and applications in variable data printing
Computer-Aided Design
Energy-based image deformation
SGP '09 Proceedings of the Symposium on Geometry Processing
Ad insertion in automatically composed documents
Proceedings of the 2012 ACM symposium on Document engineering
Hierarchical probabilistic model for news composition
Proceedings of the 2013 ACM symposium on Document engineering
Balancing font sizes for flexibility in automated document layout
Proceedings of the 2013 ACM symposium on Document engineering
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We present a new paradigm for automated document composition based on a generative, unified probabilistic document model (PDM) that models document composition. The model formally incorporates key design variables such as content pagination, relative arrangement possibilities for page elements and possible page edits. These design choices are modeled jointly as coupled random variables (a Bayesian Network) with uncertainty modeled by their probability distributions. The overall joint probability distribution for the network assigns higher probability to good design choices. Given this model, we show that the general document layout problem can be reduced to probabilistic inference over the Bayesian network. We show that the inference task may be accomplished efficiently, scaling linearly with the content in the best case. We provide a useful specialization of the general model and use it to illustrate the advantages of soft probabilistic encodings over hard one-way constraints in specifying design aesthetics.