Ensemble methods, traditionally built with independently trained de-correlated models,
have proven to be efficient methods for reducing the remaining residual generalization error, …

Most learning algorithms require the practitioner to manually set the values of many
hyperparameters before the learning process can begin. However, with modern algorithms, the …

In the future, robots will be used more extensively as assistants in home scenarios and must
be able to acquire expertise from trainers by learning through crossmodal interaction. One …

Natural communication between humans involves hand gestures, which has an impact on
research in human-robot interaction. In a real-world scenario, understanding human gestures …

Interdisciplinary research, drawing from robotics, artificial intelligence, neuroscience,
psychology, and cognitive science, is a cornerstone to advance the state-of-the-art in multimodal …

Recently robots are being used more frequently as assistants in domestic scenarios. In this
context we train an apprentice robot to perform a cleaning task using interactive …

Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration
and received a lot of attention from the research community. For example, many neural …

Dialogue act recognition is an important part of natural language understanding. We investigate
the way dialogue act corpora are annotated and the learning approaches used so far. …

Class imbalance in machine learning is a problem often found with real-world data, where
data from one class clearly dominates the dataset. Most neural network classifiers fail to learn …

This paper presents a novel biologically-inspired approach to solving the inverse kinematics
problem efficiently on arbitrary joint chains. It provides high accuracy, convincing success …