We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis shows that the problem of ...
[15] Evgeniou, T. et. al. 2005. Learning Multiple Tasks with Kernel Methods. Journal of Machine Learning Research 2005. [16] Evgeniou, T. et. al. 2004. Regularized Multi-Task Learning. KDD2004. [17] Jacob, L. et. al. 2009. Clustered Multi-Task Learning: A Convex Formulation . NIP...
[23] Deep multi-task learning with low level tasks supervised at lower layers [24] A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks http://arxiv.org/abs/1611.01587 [25] Multi-Task Learning Using Uncertainty to Weigh...
多任务不应仅仅局限于将所有任务的知识都局限于表示为同一个参数空间,而是更加关注于如何使我们的模型学习到任务间本应该的交互模式(it is thus helpful to draw on the advances in MTL that we have discussed and enable our model to learn how the tasks should interact with each other)。 7. 辅助任务(...
多任务学习有很多形式,如联合学习(Joint Learning),自主学习(Learning to Learn),借助辅助任务学习(Learning with Auxiliary Tasks)等,这些只是其中一些别名。概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss fu...
不考虑学习共享的结构,考虑每个任务的不确定性。通过优化loss(Gaussian likelihood with task-dependant uncertainty),调节不同tasks之间的相似性。 Tensor factorisation for MTL [26] 对每层参数进行分解,为shared和task-specific Sluice Networks [27] 大杂烩(hard parameter sharing + cross stitch networks + block...
Bayesian Multitask Multiple Kernel Learning oGaussian Process Multi-task Gaussian process (MTGP) Gaussian process multi-task learning oSparse & Low Rank Methods Asymmetric Multi-Task Learning Hierarchical_Multi_Task_Learning Asynchronous Multi-Task Learning HMTL: Hierarchical...
不考虑学习共享的结构,考虑每个任务的不确定性。通过优化loss(Gaussian likelihood with task-dependant uncertainty),调节不同tasks之间的相似性。 Tensor factorisation for MTL [26] 对每层参数进行分解,为shared和task-specific Sluice Networks [27] 大杂烩(hard parameter sharing+ cross stitch networks + block-...
Multiple Kernel Learning (MKL) is a machine learning approach that allows for the integration of multiple features, such as genes, proteins, and metabolites, by combining them as different kernel matrices. These matrices are then used as input for various inference tasks, such as classification and...
Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks. Expressivity of a machine learning model, referring to the ability of the model to approximate complex functions, has a significant influen...