Designing logic circuits for probabilistic computation in the presence of noise
Proceedings of the 42nd annual Design Automation Conference
Designing MRF based error correcting circuits for memory elements
Proceedings of the conference on Design, automation and test in Europe: Proceedings
A probabilistic-based design for nanoscale computation
Nano, quantum and molecular computing
Multilevel coarse-to-fine PCFG parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A rate and history-preserving resampling algorithm for neural spike trains
Neural Computation
Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework
Journal of Visual Communication and Image Representation
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
| Hi-index | 754.84 |
Graphical representations of codes facilitate the design of computationally efficient decoding algorithms. This is an example of a general connection between dependency graphs, as arise in the representations of Markov random fields, and the dynamic programming principle. We concentrate on two computational tasks: finding the maximum-likelihood codeword and finding its posterior probability, given a signal received through a noisy channel. These two computations lend themselves to a particularly elegant version of dynamic programming, whereby the decoding complexity is particularly transparent. We explore some codes and some graphical representations designed specifically to facilitate computation. We further explore a coarse-to-fine version of dynamic programming that can produce an exact maximum-likelihood decoding many orders of magnitude faster than ordinary dynamic programming