Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …

Process dynamic behaviors resulting from closed-loop control and the inherence of
processes are ubiquitous in industrial processes and bring a considerable challenge for …

Abstract Representation learning and option discovery are two of the biggest challenges in
reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for …

In the absence of external guidance, how can a robot learn to map the many raw pixels of
high-dimensional visual inputs to useful action sequences? We propose here Continual …

We introduce here an incremental version of slow feature analysis (IncSFA), combining
candid covariance-free incremental principal components analysis (CCIPCA) and …

Abstract Slow Feature Analysis (SFA) is a subspace learning method inspired by the human
visual system, however, it is seldom seen in computer vision. Motivated by its application for …

Abstract We show that Incremental Slow Feature Analysis (IncSFA) provides a low
complexity method for learning Proto-Value Functions (PVFs). It has been shown that a …

Computational neuroscience studies have examined the human visual system through
functional magnetic resonance imaging (fMRI) and identified a model where the mammalian …

How can a humanoid robot autonomously learn and refine multiple sensorimotor skills as a
byproduct of curiosity driven exploration, upon its high-dimensional unprocessed visual …

Abstraction is an important aspect of intelligence which enables agents to construct robust
representations for effective and efficient decision making. Although, deep neural networks …