18 citations · 82 across the 26 of their papers we have counts for
6 papers · 1 filter
Linear Memory Networks
Davide Bacciu, Antonio Carta, Alessandro Sperduti
Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memo…
Text Summarization as Tree Transduction by Top-Down TreeLSTM
Davide Bacciu, Antonio Bruno
Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem…
Learning Tree Distributions by Hidden Markov Models
Davide Bacciu, Daniele Castellana
Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main app…
Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs
Davide Bacciu, Andrea Bongiorno
The paper introduces concentric Echo State Network, an approach to design reservoir topologies that tries to bridge the gap between deterministically constructed simple cycle model…
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Davide Bacciu, Federico Errica, Alessio Micheli
We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive…
Bioinformatics and Medicine in the Era of Deep Learning
Davide Bacciu, Paulo J. G. Lisboa, José D. Martín +2
Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatic…