PPATuner: pareto-driven tool parameter auto-tuning in physical design via gaussian process transfer learning.
Hao GengQi XuTsung-Yi HoBei YuPublished in: DAC (2022)
Keyphrases
- physical design
- transfer learning
- gaussian process
- design tools
- gaussian processes
- design methodology
- learning tasks
- semi supervised
- labeled data
- query optimization
- multi task
- bayesian framework
- semi supervised learning
- text categorization
- multi task learning
- model selection
- active learning
- reinforcement learning
- database administrators
- regression model
- collaborative filtering
- machine learning
- learning algorithm
- hyperparameters
- unlabeled data
- latent variables
- text classification
- neural network
- data sets
- supervised learning
- database
- text mining
- parameter settings
- query processing
- bayesian networks
- closed form
- data mining
- machine learning algorithms