439 citations · 1.4k across the 66 of their papers we have counts for
4 papers · 1 filter
Learn to Synchronize, Synchronize to Learn
Pietro Verzelli, Cesare Alippi, Lorenzo Livi
In recent years, the machine learning community has seen a continuous growing interest in research aimed at investigating dynamical aspects of both training procedures and machine…
Graph Neural Networks in TensorFlow and Keras with Spektral
Daniele Grattarola, Cesare Alippi
In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implem…
Input-to-State Representation in linear reservoirs dynamics
Pietro Verzelli, Cesare Alippi, Lorenzo Livi +1
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is…
Deep Reinforcement Learning with Weighted Q-Learning
Andrea Cini, Carlo D'Eramo, Jan Peters +1
Reinforcement learning algorithms based on Q-learning are driving Deep Reinforcement Learning (DRL) research towards solving complex problems and achieving super-human performance…