In this paper fully connected RTRL neural networks are studied. In order to learn dynamical behaviours of linear-processes or to predict time series, an autonomous learning algori...
We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suf...
We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universa...
We conducted a 2.5 week longitudinal study with five motor impaired (MI) and four non-impaired (NMI) participants, in which they learned to use the Vocal Joystick, a voice-based u...
Susumu Harada, Jacob O. Wobbrock, Jonathan Malkin,...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...