%0 Generic %1 yang2016trace %A Yang, Yongxin %A Hospedales, Timothy M. %D 2016 %K deep_learning multitask %T Trace Norm Regularised Deep Multi-Task Learning %U http://arxiv.org/abs/1606.04038 %X We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is encouraged to reuse others' parameters if possible -- this is the main motivation behind multi-task learning. In contrast to many deep multi-task learning models, we do not predefine a parameter sharing strategy by specifying which layers have tied parameters. Instead, our framework considers sharing for all shareable layers, and the sharing strategy is learned in a data-driven way.

@misc{yang2016trace, abstract = {We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is encouraged to reuse others' parameters if possible -- this is the main motivation behind multi-task learning. In contrast to many deep multi-task learning models, we do not predefine a parameter sharing strategy by specifying which layers have tied parameters. Instead, our framework considers sharing for all shareable layers, and the sharing strategy is learned in a data-driven way.}, added-at = {2018-04-27T11:26:51.000+0200}, author = {Yang, Yongxin and Hospedales, Timothy M.}, biburl = {https://www.bibsonomy.org/bibtex/206e1be82720cee95616e1662d8842971/dallmann}, description = {Trace Norm Regularised Deep Multi-Task Learning}, interhash = {656ff80d9ba8bd08806ccff9393b2754}, intrahash = {06e1be82720cee95616e1662d8842971}, keywords = {deep_learning multitask}, note = {cite arxiv:1606.04038Comment: Submission to Workshop track - ICLR 2017}, timestamp = {2018-04-27T11:26:51.000+0200}, title = {Trace Norm Regularised Deep Multi-Task Learning}, url = {http://arxiv.org/abs/1606.04038}, year = 2016 }