Zobrazeno 1 - 10
of 83
pro vyhledávání: '"MAISTRO, MARIA"'
Autor:
Edin, Joakim, Motzfeldt, Andreas Geert, Christensen, Casper L., Ruotsalo, Tuukka, Maaløe, Lars, Maistro, Maria
Deep neural network predictions are notoriously difficult to interpret. Feature attribution methods aim to explain these predictions by identifying the contribution of each input feature. Faithfulness, often evaluated using the area over the perturba
Externí odkaz:
http://arxiv.org/abs/2408.08137
Autor:
Marjanović, Sara Vera, Yu, Haeun, Atanasova, Pepa, Maistro, Maria, Lioma, Christina, Augenstein, Isabelle
Knowledge-intensive language understanding tasks require Language Models (LMs) to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated knowledge. However, conflicting knowledge can be present in the LM's pa
Externí odkaz:
http://arxiv.org/abs/2407.17023
Autor:
Edin, Joakim, Maistro, Maria, Maaløe, Lars, Borgholt, Lasse, Havtorn, Jakob D., Ruotsalo, Tuukka
Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantly enhanced the processing of such records, streamlining workflows and
Externí odkaz:
http://arxiv.org/abs/2406.08958
Relevance and fairness are two major objectives of recommender systems (RSs). Recent work proposes measures of RS fairness that are either independent from relevance (fairness-only) or conditioned on relevance (joint measures). While fairness-only me
Externí odkaz:
http://arxiv.org/abs/2405.18276
While recommender systems with multi-modal item representations (image, audio, and text), have been widely explored, learning recommendations from multi-modal user interactions (e.g., clicks and speech) remains an open problem. We study the case of m
Externí odkaz:
http://arxiv.org/abs/2405.04246
Publikováno v:
ACM Transactions on Recommender Systems 2023
Providing personalized recommendations for insurance products is particularly challenging due to the intrinsic and distinctive features of the insurance domain. First, unlike more traditional domains like retail, movie etc., a large amount of user fe
Externí odkaz:
http://arxiv.org/abs/2403.00368
Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we examine existing evaluation measures of fairness in recommender systems
Externí odkaz:
http://arxiv.org/abs/2311.01013
Autor:
Edin, Joakim, Junge, Alexander, Havtorn, Jakob D., Borgholt, Lasse, Maistro, Maria, Ruotsalo, Tuukka, Maaløe, Lars
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this adminis
Externí odkaz:
http://arxiv.org/abs/2304.10909
Autor:
Bruun, Simone Borg, Lesniak, Kacper Kenji, Biasini, Mirko, Carmignani, Vittorio, Filianos, Panagiotis, Lioma, Christina, Maistro, Maria
Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses struggle to apply state-of-the-art models due to their very limited availab
Externí odkaz:
http://arxiv.org/abs/2301.11009
Autor:
Biasini, Mirko, Carmignani, Vittorio, Ferro, Nicola, Filianos, Panagiotis, Maistro, Maria, di Nunzio, Giorgio Maria
We present FullBrain, a social e-learning platform where students share and track their knowledge. FullBrain users can post notes, ask questions and share learning resources in dedicated course and concept spaces. We detail two components of FullBrai
Externí odkaz:
http://arxiv.org/abs/2212.01387