Agents that exist in an environment that changes over time, and are able to take into account the temporal nature of experience, are commonly called incremental learners. It is wid...
Nicola Di Mauro, Floriana Esposito, Stefano Ferill...
Abstract. Recent experimental observations of spiketiming-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of ...
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propos...
Background: Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on differen...
Marc Vincent, Andrea Passerini, Matthieu Labb&eacu...