例句與造句

1. Probabilistic graphical models form a large class of structured prediction models.
2. In 2009, Friedman and Probabilistic Graphical Models.
3. In 2009, she published a textbook on probabilistic graphical models together with Nir Friedman.
4. He specializes in deep learning, probabilistic graphical models, and large-scale optimization.
5. PyMC is an open source Probabilistic Graphical Model with advanced features and easy to use interface.
6. It's difficult to find probabilistic graphical model in a sentence. 用probabilistic graphical model造句挺難的
7. "' Credal networks "'are probabilistic graphical models based on imprecise probability.
8. By modeling sudoku codes as a probabilistic graphical model belief propagation can be used for Sudoku codes.
9. Sudoku codes can be represented by probabilistic graphical model in which they take the form of a low-density parity-check code.
10. Sudokus can be modeled as a probabilistic graphical model and thus methods from decoding low-density parity-check codes like belief propagation can be used.
11. Bivariate and multivariate distributions are usually represented as Probabilistic Graphical Models ( graphs ), in which edges denote statistical dependencies ( or conditional probabilities ) and vertices denote variables.
12. Research in this area has been extended to the use of Bayesian networks ( BNs ), which are probabilistic graphical models typically designed to model cause and effect or dependent relationships.
13. Such models are learned from the population by employing machine learning techniques and represented as Probabilistic Graphical Models, from which new solutions can be sampled or generated from guided-crossover.
14. Relying on an efficient thermal state preparation protocol starting from an arbitrary state, quantum-enhanced Markov logic networks exploit the symmetries and the locality structure of the probabilistic graphical model generated by a first-order logic template.
15. Among his research contributions were the invention of error-correcting output coding to multi-class classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models.