ATLM: Advances in Optimizing and Transfer Learning Models

This workshop will cover original and pioneering contributions, theory as well as applications on optimizing, combining, and transferring learning models, and aim at an inspiring discussion on the recent progress and the future developments. Learning models, especially those based on different paradigms, can be combined and optimized for improving their accuracy. Thus, each learning method imposes specific modeling from observations which translates to a set of constrains. However, such assumptions may lead to weak and non adapted learners if they are not satisfied. In many cases, the ill-posed of the learning process and the data partiality of observations make the optimization methods converge to different solutions and subsequently fail under various circumstances. The workshop will be a good opportunity, to discuss recent advances in optimizing and learning models. Furthermore, the effectiveness of these methods will be discussed considering the concepts of diversity and selection of these approaches.