Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Brigato, P."'
The advancement of artificial intelligence (AI) in food and nutrition research is hindered by a critical bottleneck: the lack of annotated food data. Despite the rise of highly efficient AI models designed for tasks such as food segmentation and clas
Externí odkaz:
http://arxiv.org/abs/2410.19756
We introduce Tune without Validation (Twin), a pipeline for tuning learning rate and weight decay without validation sets. We leverage a recent theoretical framework concerning learning phases in hypothesis space to devise a heuristic that predicts w
Externí odkaz:
http://arxiv.org/abs/2403.05532
Autor:
Dack, Ethan, Brigato, Lorenzo, McMurray, Matthew, Fontanellaz, Matthias, Frauenfelder, Thomas, Hoppe, Hanno, Exadaktylos, Aristomenis, Geiser, Thomas, Funke-Chambour, Manuela, Christe, Andreas, Ebner, Lukas, Mougiakakou, Stavroula
The pandemic resulted in vast repositories of unstructured data, including radiology reports, due to increased medical examinations. Previous research on automated diagnosis of COVID-19 primarily focuses on X-ray images, despite their lower precision
Externí odkaz:
http://arxiv.org/abs/2309.01740
Solving image classification tasks given small training datasets remains an open challenge for modern computer vision. Aggressive data augmentation and generative models are among the most straightforward approaches to overcoming the lack of data. Ho
Externí odkaz:
http://arxiv.org/abs/2309.01694
Autor:
Rahman, Lubnaa Abdur, Papathanail, Ioannis, Brigato, Lorenzo, Spanakis, Elias K., Mougiakakou, Stavroula
Food recognition and nutritional apps are trending technologies that may revolutionise the way people with diabetes manage their diet. Such apps can monitor food intake as a digital diary and even employ artificial intelligence to assess the diet aut
Externí odkaz:
http://arxiv.org/abs/2307.05372
Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and ex
Externí odkaz:
http://arxiv.org/abs/2212.12478
Autor:
Gianluca Vadalà, Giuseppe Francesco Papalia, Fabrizio Russo, Paolo Brigato, Luca Ambrosio, Rocco Papalia, Vincenzo Denaro
Publikováno v:
Neurospine, Vol 21, Iss 1, Pp 76-82 (2024)
Objective Several studies have advocated for the higher accuracy of transpedicular screw placement under cone-beam computed tomography (CBCT) compared to conventional 2-dimensional (2D) fluoroscopy. The superiority of navigation systems in perioperat
Externí odkaz:
https://doaj.org/article/3f203e23c9014d4dabc731b148784c20
Autor:
Brigato, Lorenzo, Iocchi, Luca
Deep neural networks represent the gold standard for image classification. However, they usually need large amounts of data to reach superior performance. In this work, we focus on image classification problems with a few labeled examples per class a
Externí odkaz:
http://arxiv.org/abs/2111.14493
Learning from limited amounts of data is the hallmark of intelligence, requiring strong generalization and abstraction skills. In a machine learning context, data-efficient methods are of high practical importance since data collection and annotation
Externí odkaz:
http://arxiv.org/abs/2109.13561
Data-efficient image classification using deep neural networks in settings, where only small amounts of labeled data are available, has been an active research area in the recent past. However, an objective comparison between published methods is dif
Externí odkaz:
http://arxiv.org/abs/2108.13122