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20092024
most citedLaplacian Support Vector Machines Trained in the Primal

316 citations · 348 across the 15 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.LG201910 cited

Asynchronous Distributed Learning from Constraints

Francesco Farina, Stefano Melacci, Andrea Garulli +1

In this paper, the extension of the framework of Learning from Constraints (LfC) to a distributed setting where multiple parties, connected over the network, contribute to the lear…

cs.LG2019

Jointly Learning to Detect Emotions and Predict Facebook Reactions

Lisa Graziani, Stefano Melacci, Marco Gori

The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Visio…

cs.CL201910 cited

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…

cs.CV2019

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…

cs.CL2019

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…

cs.CV2019

Learning Visual Features Under Motion Invariance

Alessandro Betti, Marco Gori, Stefano Melacci

Humans are continuously exposed to a stream of visual data with a natural temporal structure. However, most successful computer vision algorithms work at image level, completely di…