Transfer Learning for Depression: Early Detection and Severity Prediction from Social Media Postings

Second, in order to improve the learning convergence for the target semantic slot filling (SF) task, models trained for dif- ferent tasks, such as automatic speech recognition and

Dans le chapitre 3, nous proposerons un nouveau modèle d’apprentissage multi- tâche, afin de résoudre le problème d’apprentissage par transfert pour SVM 1-classe avec un changement

In our experiments, this version worked better in the Unsupervised and Transfer Learning Challenge, characterized by a non-discriminant linear classifier and very few labeled

Nous adaptons une approche bas ´ee sur le m ´eta-apprentissage pour ´etudier le probl `eme de la d ´etection des discours de haine et de langage of- fensant de type few-shot dans

the transfer learning from a large music dataset (Magnatagatune is used in this paper), our model shows a faster convergence and higher average accuracy than the same model of

The results obtained for this task, along with those based on ranking measures, showed us strong evidence that SS3 properly values words in relation to how relevant they are to

The source of test set was also based on the data provided in eRisk 2017 and 2018, but it was released item by item, through a news feed simulation server provided by the

we proposed Deep Mood Evaluation Module (DMEM) that uses attention based deep learning models to construct a time series rep- resenting temporal mood variation through users posts