Machine learning of inductive bias
Machine learning of inductive bias
Machine Learning - Special issue on inductive transfer
Learning to learn
Empirical Bayes for Learning to Learn
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discriminability-Based Transfer between Neural Networks
Advances in Neural Information Processing Systems 5, [NIPS Conference]
The Task Rehearsal Method of Life-Long Learning: Overcoming Impoverished Data
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Multi-task feature and kernel selection for SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Image morphing: transfer learning between tasks that have multiple outputs
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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Previous research has investigated inductive transfer for single output modeling problems such as classification or prediction of a scalar. Little research has been done in the area of inductive transfer applied to tasks with multiple outputs. We report the results of using Multiple Task Learning (MTL) neural networks and Context-sensitive Multiple Task Learning (csMTL) on a domain of image transformation tasks. Models are developed to transform synthetic images of neutral (passport) faces to that of corresponding images of angry, happy and sad faces. The results are inconclusive for MTL, however they demonstrate that inductive transfer with csMTL is beneficial. When the secondary tasks have sufficient numbers of training examples from which to provide transfer, csMTL models are able to transform images more accurately than standard single task learning models.