Name
Abstract | ||
|---|---|---|
In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multit... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2016.2641160 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Bayes methods,Sparse matrices,Probabilistic logic,Principal component analysis,Learning systems,Correlation,Computers | Multi-task learning,Pattern recognition,Inference,Computer science,Exploit,Artificial intelligence,Statistical model,Variational Bayesian methods,Probabilistic logic,Discriminative model,Sparse matrix,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 3 | 2162-237X |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
4 | ||