suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification … learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study … k -means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task …

present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach …. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task … learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the …

present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach …. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task … learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the …

. Based on this finding, we propose a novel hierarchical multi-task learning approach to refine fraud-detection systems … a multi-task learning approach with two prediction layers. The first layer provides the probabilities of general fraud …

into the considered conditions. The Bayes framework and the Multi-task Learning models are used to obtain the process curve …, which map the process curve densities with different conditions on each other. The latent space is formed via Multi-Task-Learning …