Luca Pedrelli
Luca Pedrelli
Postdoctoral Researcher, Inria
Deep learning models are typically characterized by layers of neurons. Each layer provides a more abstract representation of the input information. This allows the model to solve a global task by addressing a progression of increasingly abstract problems. In the case of time-series processing, deep recurrent architectures are able to develop a multiple time-scales representation. In this talk, we present Deep Echo State Networks as a tool to analyze and design efficient deep recurrent architectures for real-world tasks concerning time-series and sequence modeling.