Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...
Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...
The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...
The presence of conversational agents (or chatbots) in educational contexts has been steadily increasing over the past few years. Recent surveys have shown widespread interest in the use of chatbots in education, both for research and practice. Although th ...
Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...
Language has shaped human evolution and led to the desire to endow machines with language abilities. Recent advancements in natural language processing enable us to achieve this breakthrough in human-machine interaction. However, introducing conversational ...
With their exponentially rising computational power, digital platforms are heralding a new era of hybrid intelligence. There has recently been much enthusiasm and hype that the Metaverse has the potential to unlock hybrid intelligence. This is premised on ...
A force-feedback surface that creates and modulates distinctive profile and stiffnessto interact with a user in contact thereto, the surface being functionally independentto be used as a single module but can be customized to extend the application indiver ...
Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...
Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...
