36 citations · 149 across the 21 of their papers we have counts for
7 papers · 1 filter
Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances
Marco Maggini, Giuseppe Marra, Stefano Melacci +1
We consider a scenario where an artificial agent is reading a stream of text composed of a set of narrations, and it is informed about the identity of some of the individuals that…
An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning
Giuseppe Marra, Andrea Zugarini, Stefano Melacci +1
In the last few years, neural networks have been intensively used to develop meaningful distributed representations of words and contexts around them. When these representations, a…
On the relation between Loss Functions and T-Norms
Francesco Giannini, Giuseppe Marra, Michelangelo Diligenti +2
Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing. A key element of this success has been the devel…
T-Norms Driven Loss Functions for Machine Learning
Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti +2
Neural-symbolic approaches have recently gained popularity to inject prior knowledge into a learner without requiring it to induce this knowledge from data. These approaches can po…
Neural Markov Logic Networks
Giuseppe Marra, Ondřej Kuželka
We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an ex…
LYRICS: a General Interface Layer to Integrate Logic Inference and Deep Learning
Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti +1
In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some ki…
Integrating Learning and Reasoning with Deep Logic Models
Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti +1
Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic l…