scholarly journals Multi-task learning deep neural networks for speech feature denoising

2015 ◽  
Author(s):  
Bin Huang ◽  
Dengfeng Ke ◽  
Hao Zheng ◽  
Bo Xu ◽  
Yanyan Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Task Learning ◽  
Speech Feature

Rapid adaptation for deep neural networks through multi-task learning

2015 ◽  
Author(s):  
Zhen Huang ◽  
Jinyu Li ◽  
Sabato Marco Siniscalchi ◽  
I-Fan Chen ◽  
Ji Wu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Rapid Adaptation ◽  
Task Learning

scholarly journals Latent Multi-Task Architecture Learning

2019 ◽  
Vol 33 ◽  
pp. 4822-4829 ◽  
Author(s):  
Sebastian Ruder ◽  
Joachim Bingel ◽  
Isabelle Augenstein ◽  
Anders Søgaard
Keyword(s):  
Neural Networks ◽  
Recent Work ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Approaches To Learning ◽  
Average Error ◽  
Task Learning ◽  
Task Architecture ◽  
Parameter Sharing

Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)–(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15% average error reductions over common approaches to MTL.

Multi-task learning for the prediction of wind power ramp events with deep neural networks

2020 ◽  
Vol 123 ◽  
pp. 401-411 ◽  
Author(s):  
M. Dorado-Moreno ◽  
N. Navarin ◽  
P.A. Gutiérrez ◽  
L. Prieto ◽  
A. Sperduti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Wind Power ◽  
Deep Neural Networks ◽  
Task Learning ◽  
Ramp Events ◽  
Wind Power Ramp Events

Dataset-aware multi-task learning approaches for biomedical named entity recognition

2020 ◽  
Vol 36 (15) ◽  
pp. 4331-4338
Author(s):  
Mei Zuo ◽  
Yang Zhang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Quality Data ◽  
Supplementary Information ◽  
Named Entity ◽  
Task Learning ◽  
Biomedical Named Entity Recognition

Abstract Motivation Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information from various datasets of Bio-NER. The performance of state-of-the-art MTL method was significantly limited by the number of training datasets. Results We propose two dataset-aware MTL approaches for Bio-NER which jointly train all models for numerous Bio-NER datasets, thus each of these models could discriminatively exploit information from all of related training datasets. Both of our two approaches achieve substantially better performance compared with the state-of-the-art MTL method on 14 out of 15 Bio-NER datasets. Furthermore, we implemented our approaches by incorporating Bio-NER and biomedical part-of-speech (POS) tagging datasets. The results verify Bio-NER and POS can significantly enhance one another. Availability and implementation Our source code is available at https://github.com/zmmzGitHub/MTL-BC-LBC-BioNER and all datasets are publicly available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Multi-task Learning in Deep Neural Networks at EVALITA 2018

2018 ◽  
pp. 86-95 ◽  
Author(s):  
Andrea Cimino ◽  
Lorenzo De Mattei ◽  
Felice Dell’Orletta
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Task Learning
Sign in / Sign up

Export Citation Format

Share Document