Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Zanga, Alessio"'
Publikováno v:
ACM Trans. Recomm. Syst. 2, 2, Article 17 (June 2024), 34 pages
Causality is receiving increasing attention in the Recommendation Systems (RSs) community, which has realised that RSs could greatly benefit from causality to transform accurate predictions into effective and explainable decisions. Indeed, the RS lit
Externí odkaz:
http://arxiv.org/abs/2410.01822
Since its introduction to the public, ChatGPT has had an unprecedented impact. While some experts praised AI advancements and highlighted their potential risks, others have been critical about the accuracy and usefulness of Large Language Models (LLM
Externí odkaz:
http://arxiv.org/abs/2407.18607
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almos
Externí odkaz:
http://arxiv.org/abs/2311.08427
Autor:
Zanga, Alessio, Bernasconi, Alice, Lucas, Peter J. F., Pijnenborg, Hanny, Reijnen, Casper, Scutari, Marco, Stella, Fabio
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain mi
Externí odkaz:
http://arxiv.org/abs/2305.10050
Autor:
Zanga, Alessio, Bernasconi, Alice, Lucas, Peter J. F., Pijnenborg, Hanny, Reijnen, Casper, Scutari, Marco, Stella, Fabio
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical
Externí odkaz:
http://arxiv.org/abs/2305.10041
Autor:
Zanga, Alessio, Stella, Fabio
Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designe
Externí odkaz:
http://arxiv.org/abs/2305.10032
Autor:
Cisotto, Giulia, Trentini, Andrea, Zoppis, Italo, Zanga, Alessio, Manzoni, Sara, Pietrabissa, Giada, Usubini, Anna Guerrini, Castelnuovo, Gianluca
As the worldwide population gets increasingly aged, in-home telemedicine and mobile-health solutions represent promising services to promote active and independent aging and to contribute to a paradigm shift towards patient-centric healthcare. In thi
Externí odkaz:
http://arxiv.org/abs/2102.08692
Autor:
Cisotto, Giulia, Zanga, Alessio, Chlebus, Joanna, Zoppis, Italo, Manzoni, Sara, Markowska-Kaczmar, Urszula
Objective: To evaluate the impact on Electroencephalography (EEG) classification of different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We compared three attention-enhanced DL models, the brand-new InstaGATs, an LSTM with a
Externí odkaz:
http://arxiv.org/abs/2012.01074
Publikováno v:
In International Journal of Approximate Reasoning December 2022 151:101-129
Autor:
Zanga, Alessio, Bernasconi, Alice, Lucas, Peter J. F., Pijnenborg, Hanny, Reijnen, Casper, Scutari, Marco, Stella, Fabio
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain mi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bfa90e650f4eaf9a367577b40a1d681
http://arxiv.org/abs/2305.10050
http://arxiv.org/abs/2305.10050