Our study suggests that stimulus-driven prioritization of learning tasks is in line with the predictions of selective and limited attention theories, and provides a key example of interference in dual-task learning by an arthropod.doi:10.1016/j.anbehav.2017.09.005Inga C Christiansen...
We introduce a regularization technique to improve system identification for dual-task learning with recurrent neural networks. In particular, the method is introduced using the Factored Tensor Recurrent Neural Networks first presented in [1]. Our goal is to identify a dynamical system with few ...
第三,对偶学习和多任务学习(multi-task learning)也不相同。尽管多任务学习也是同时学习多个任务共的模型,但这些任务必须共享相同的输入空间,而对偶学习对输入空间没有要求,只要这些任务能形成一个闭环系统即可。第四,对偶学习和迁移学习(transfer learning)也很不一样。迁移学习用一个或多个相关的任务来辅助主要任务的...
The success of deep learning methods in medical image segmentation tasks usually requires a large amount of labeled data. However, obtaining reliable annotations is expensive and time-consuming. Semi-supervised learning has attracted much attention in me
通过引入了CG任务,利用Multi-Task Learning和Dual learning,同时促进了Code Summary和Code Retrieval的表现 在SQL和Python两个数据集上进行试验,结果表明确实有较大提升 通过消融试验,证明了Dual learning和Muti-Task learning的有效性 Background 这里作者提到了三个任务,简要说明一下 ...
刘铁岩老师在他的ppt里展示的summary里面提到了Unsupervised Learning/Co-training/multi-task learning/...
DTIL-Net: Dual-Task Interactive Learning Network for Automated Grading of Diabetic Retinopathy and Macular Edema 来自 Springer 喜欢 0 阅读量: 4 作者:J Long,Y Tan,S Song,H Xia 摘要: Diabetic retinopathy (DR) has been the leading cause of blindness associated with a common complication of ...
To improve the learning capability of the approach, we proposed to conduct dual-task learning, i.e., categorical and ordinal classification, using two loss functions that are tailored to each classification task. In order to further improve the generalizability of the approach, we also proposed a...
Observing that multi/dual-task learning attends to various levels of information which have inherent prediction perturbation, we ask the question in this work: can we explicitly build task-level regularization rather than implicitly constructing networks- and/or data-level perturbation-and-transformation ...
近日,顶级国际会议 NeurIPS 的 The Machine Learning for Combinatorial Optimization(以下简称:ML4CO) 组合优化比赛结果揭幕,来自旷视研究院的代表队荣获 Dual Task 赛道冠军。 ML4CO 全称基于机器学习的组合优化,本次比赛由加拿大蒙特利尔理工大学和蒙特利尔大学机器学习研究所 (Mila) 主办。Mila是全球领先的深度学习研究...