584 citations · 1.5k across the 28 of their papers we have counts for
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Feature Selection for Classification with QAOA
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of r…
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini +3
Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking o…
GAN-based Matrix Factorization for Recommender Systems
Ervin Dervishaj, Paolo Cremonesi
Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-…
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferen…