18 citations · 82 across the 26 of their papers we have counts for
12 papers · 1 filter
Predictive Auto-scaling with OpenStack Monasca
Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta +2
Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a pre…
Inductive learning for product assortment graph completion
Haris Dukic, Georgios Deligiorgis, Pierpaolo Sepe +2
Global retailers have assortments that contain hundreds of thousands of products that can be linked by several types of relationships like style compatibility, "bought together", "…
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation
Marco Podda, Davide Bacciu
The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several…
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
Davide Bacciu, Siranush Akarmazyan, Eric Armengaud +32
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment c…
Calliope -- A Polyphonic Music Transformer
Andrea Valenti, Stefano Berti, Davide Bacciu
The polyphonic nature of music makes the application of deep learning to music modelling a challenging task. On the other hand, the Transformer architecture seems to be a good fit…
Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss
Elisa Ferrari, Davide Bacciu
Resilience to class imbalance and confounding biases, together with the assurance of fairness guarantees are highly desirable properties of autonomous decision-making systems with…