17级付求爱同学中稿IJCNN 2019
IEEE International Joint Conference on Neural Networks (IJCNN 2019)将于2019年7月14日至7月19日，在匈牙利布达佩斯举行。IJCNN是神经网络及相关领域的研究人员和其他专业人士的首要国际会议，也被CCF推荐为人工智能方向的C类会议。
17级同学付求爱在实验室雷凯老师指导下，完成一篇长文”Multi-Task Learning with Capsule Networks”，并以确认被IJCNN 2019录用！中稿论文的简介如下：
论文标题: Multi-Task Learning with Capsule Networks
论文作者: Kai Lei, Qiuai Fu, Yuzhi Liang*
英文摘要: Multi-task learning is a machine learning approach learning multiple tasks jointly while exploiting commonalities and differences across tasks. A shared representation is learned by multi-task learning, and what is learned for each task can help other tasks be learned better. Most of existing multi-task learning methods adopt deep neural network as the classifier of each task. However, a deep neural network can exploit its strong curve-fitting capability to achieve high accuracy in training data even when the learned representation is not good enough. This is contradictory to the purpose of multi-task learning. In this paper, we propose a framework named multi-task capsule (MT-Capsule) which improves multi-task learning with capsule network. Capsule network is a new architecture which can intelligently model part-whole relationships to constitute viewpoint invariant knowledge and automatically extend the learned knowledge to different...