Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Cerquitelli Tania"'
Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network
Externí odkaz:
http://arxiv.org/abs/2409.15973
Autor:
Cambrin, Daniele Rege, Poeta, Eleonora, Pastor, Eliana, Cerquitelli, Tania, Baralis, Elena, Garza, Paolo
Segmentation of crop fields is essential for enhancing agricultural productivity, monitoring crop health, and promoting sustainable practices. Deep learning models adopted for this task must ensure accurate and reliable predictions to avoid economic
Externí odkaz:
http://arxiv.org/abs/2408.07040
AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as black-box systems, complicatin
Externí odkaz:
http://arxiv.org/abs/2407.01697
Concept Drift is a phenomenon in which the underlying data distribution and statistical properties of a target domain change over time, leading to a degradation of the model's performance. Consequently, models deployed in production require continuou
Externí odkaz:
http://arxiv.org/abs/2406.17813
Autor:
Koudounas, Alkis, Ciravegna, Gabriele, Fantini, Marco, Succo, Giovanni, Crosetti, Erika, Cerquitelli, Tania, Baralis, Elena
Publikováno v:
Proc. Interspeech 2024, 3040-3044
Voice disorders are pathologies significantly affecting patient quality of life. However, non-invasive automated diagnosis of these pathologies is still under-explored, due to both a shortage of pathological voice data, and diversity of the recording
Externí odkaz:
http://arxiv.org/abs/2406.14693
Kolmogorov-Arnold Networks (KANs) have very recently been introduced into the world of machine learning, quickly capturing the attention of the entire community. However, KANs have mostly been tested for approximating complex functions or processing
Externí odkaz:
http://arxiv.org/abs/2406.14529
Autor:
De Santis, Francesco, Bich, Philippe, Ciravegna, Gabriele, Barbiero, Pietro, Giordano, Danilo, Cerquitelli, Tania
Despite their success, Large-Language Models (LLMs) still face criticism as their lack of interpretability limits their controllability and reliability. Traditional post-hoc interpretation methods, based on attention and gradient-based analysis, offe
Externí odkaz:
http://arxiv.org/abs/2406.14335
The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanations has been disputed in several works lately, advocating for more user
Externí odkaz:
http://arxiv.org/abs/2312.12936
Autor:
Vargas-Solar, Genoveva, Cerquitelli, Tania, Espinosa-Oviedo, Javier A., Cheval, François, Buchaille, Anthelme, Polgar, Luca
This paper proposes a conversational approach implemented by the system Chatin for driving an intuitive data exploration experience. Our work aims to unlock the full potential of data analytics and artificial intelligence with a new generation of dat
Externí odkaz:
http://arxiv.org/abs/2311.06695