Abstract Representation learning has emerged as a versatile tool that is able to take
advantage of the vast datasets acquired using digital technologies. The broad applicability …

For average human, it is often easy to use prior knowledge in learning to perform new task,
like we do when distinguishing objects using features extracted from visual information. As …

Standard meta-learning for representation learning aims to find a common representation to
be shared across multiple tasks. The effectiveness of these methods is often limited when …

To address the problem of algorithm selection for the classification task, we equip a
relational case base with new similarity measures that are able to cope with multirelational …

Meta-learning algorithms adapt quickly to new tasks that are drawn from the same task
distribution as the training tasks. The mechanism leading to fast adaptation is the …

Meta-learning has many aspects, but its final goal is to discover in an automatic way many
interesting models for a given data. Our early attempts in this area involved heterogeneous …

Meta-learning has emerged as an efficient approach for constructing target models based
on support sets. For example, the meta-learned embeddings enable the construction of …

Machine Learning algorithms have had a profound impact on the field of computer science
over the past few decades. These algorithms performance is greatly influenced by the …

Machine learning algorithms have had a profound impact on the field of computer science
over the past few decades. The performance of these algorithms heavily depends on the …

My research focuses on promoting the advancement of intelligent computational systems
with better awareness of commonsense and expert knowledge, which leads to more efficient …