Publications:Multi-Task Representation Learning
From ISLAB/CAISR
| Title | Multi-Task Representation Learning |
|---|---|
| Author | Mohamed-Rafik Bouguelia and Sepideh Pashami and Sławomir Nowaczyk |
| Year | 2017 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | |
| Conference | 30th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS) |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1205474 |
| Abstract | The majority of existing machine learning algorithms assume that training examples are already represented with sufficiently good features, in practice ones that are designed manually. This traditional way of preprocessing the data is not only tedious and time consuming, but also not sufficient to capture all the different aspects of the available information. With big data phenomenon, this issue is only going to grow, as the data is rarely collected and analyzed with a specific purpose in mind, and more often re-used for solving different problems. Moreover, the expert knowledge about the problem which allows them to come up with good representations does not necessarily generalize to other tasks. Therefore, much focus has been put on designing methods that can automatically learn features or representations of the data instead of learning from handcrafted features. However, a lot of this work used ad hoc methods and the theoretical understanding in this area is lacking. |
