One proposed method uses the projection distance from the manifold to regularize the task parameters. The manifold structure and the task parameters are learned ...

We present a novel method for multitask learning (MTL) based on manifold regu- larization: assume that all task parameters lie on a manifold.

This paper develops multi-task vector field learning (MTVFL) which learns the predictor functions and the vector fields simultaneously and formalizes the ...

Nov 3, 2014 · I was reading the paper "Learning multiple tasks using Manifold regularization"(http://www.umiacs.umd.edu/~arvinda/mysite/papers/nipsMTL.pdf).

PDF | On Jan 1, 2010, Arvind Agarwal and others published Learning Multiple Tasks using Manifold Regularization. | Find, read and cite all the research you ...

We present a novel method for multitask learning (MTL) based on manifold regularization: assume that all task parameters lie on a manifold.

By training multiple tasks simultaneously, information can be better shared across tasks. This leads to significant performance improvement in many problems.

The framework can be solved by alternatively learning all tasks and the manifold structure. In particular, learning all tasks with the manifold regularization ...

Multi-task learning (MTL) aims to improve the performance of multiple tasks ... In [27], a manifold regularized multi-task learning model has been recently ...

Mar 1, 2021 · A new manifold regularization is further devised to reflect the feature-feature relation, which provides prior knowledge in multitask learning.