Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults.
Baihong JinYingshui TanYuxin ChenAlberto L. Sangiovanni-VincentelliPublished in: CoRR (2019)
Keyphrases
- monte carlo
- learning tasks
- classification models
- supervised learning
- training data
- learning models
- decision trees
- machine learning
- markov chain
- learning problems
- feature selection
- learning algorithm
- multi task
- learning experience
- machine learning algorithms
- importance sampling
- unsupervised learning
- transfer learning
- function approximation
- kernel methods
- monte carlo tree search
- feature set
- semi supervised learning
- multi label
- semi supervised
- training examples
- feature subset
- state space
- active learning
- training set
- data sets
- particle filter
- search space
- variance reduction
- data mining