Machine learning (ML) has been an important arsenal in computational biology used to
elucidate protein function for decades. With the recent burgeoning of novel ML methods and …

Annotation tools are an essential component in the creation of datasets for machine learning
purposes. Annotation tools have evolved greatly since the turn of the century, and now …

Striking progress has been made in understanding cognition by analyzing how the brain is
engaged in different modes of information processing. For instance, so-called synergistic …

Imitation learning is a discipline of machine learning primarily concerned with replicating
observed behavior of agents known to perform well on a given task, collected in …

This paper presents a model for generating expressive robot motions based on human
expressive movements. The proposed data-driven approach combines variational …

We analyze the structure of the market for foundation models, ie, large AI models such as
those that power ChatGPT and that are adaptable to downstream uses, and we examine the …

The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing
method for implementing high-level algorithms constrained by low-level neurobiological …

The ability to learn continuously is crucial for a robot to achieve a high level of intelligence
and autonomy. In this paper, we consider continual reinforcement learning (RL) for …

Most Artificial Intelligence (AI) implementations so far are based on the exploration of how
the human brain is designed. Nevertheless, while significant progress is shown on …

Multi-agent decision-making faces many challenges such as non-stationarity and sparse
rewards, while the complexity and randomness of the real environment further complicate …