227 citations · 435 across the 22 of their papers we have counts for
8 papers · 1 filter
Conditions for Unnecessary Logical Constraints in Kernel Machines
Francesco Giannini, Marco Maggini
A main property of support vector machines consists in the fact that only a small portion of the training data is significant to determine the maximum margin separating hyperplane…
Neural Poetry: Learning to Generate Poems using Syllables
Andrea Zugarini, Stefano Melacci, Marco Maggini
Motivated by the recent progresses on machine learning-based models that learn artistic styles, in this paper we focus on the problem of poem generation. This is a challenging task…
Video Surveillance of Highway Traffic Events by Deep Learning Architectures
Matteo Tiezzi, Stefano Melacci, Marco Maggini +1
In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways. The events of interest consist in a specif…
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…
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
Simone Bonechi, Paolo Andreini, Monica Bianchini +1
The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this re…