Multi-Task Learning (MTL), as opposed to Single Task Learning (STL), has become a hot topic in machine learning research. MTL has shown significant advantage to STL because of its ability to facilitate knowledge sharing between tasks. This thesis presents my recent studies on Knowledge Transfer (KT) the process of transferring knowledge from one task to another, which is at the core of MTL. The novelly proposed KT algorithm for correlated MTL adapts learner independence, thus empowering any ordinary classifier for MTL. The proposed MEB-based KT is on the basis that in the feature space, the...
Multi-Task Learning (MTL), as opposed to Single Task Learning (STL), has become a hot topic in machine learning research. MTL has shown significant ad...
This book presents an in-depth exploration of multimodal learning toward recommendation, along with a comprehensive survey of the most important research topics and state-of-the-art methods in this area.
First, it presents a semantic-guided feature distillation method which employs a teacher-student framework to robustly extract effective recommendation-oriented features from generic multimodal features. Next, it introduces a novel multimodal attentive metric learning method to model user diverse preferences for various items. Then it proposes a disentangled multimodal representation...
This book presents an in-depth exploration of multimodal learning toward recommendation, along with a comprehensive survey of the most important re...
