Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Belli, Davide"'
Autor:
Federici, Marco, Belli, Davide, van Baalen, Mart, Jalalirad, Amir, Skliar, Andrii, Major, Bence, Nagel, Markus, Whatmough, Paul
While mobile devices provide ever more compute power, improvements in DRAM bandwidth are much slower. This is unfortunate for large language model (LLM) token generation, which is heavily memory-bound. Previous work has proposed to leverage natural d
Externí odkaz:
http://arxiv.org/abs/2412.01380
In urban environments, where line-of-sight signals from GNSS satellites are frequently blocked by high-rise objects, GNSS receivers are subject to large errors in measuring satellite ranges. Heuristic methods are commonly used to estimate these error
Externí odkaz:
http://arxiv.org/abs/2402.18630
Face authentication systems require a robust anti-spoofing module as they can be deceived by fabricating spoof images of authorized users. Most recent face anti-spoofing methods rely on optimized architectures and training objectives to alleviate the
Externí odkaz:
http://arxiv.org/abs/2207.12272
Autor:
Belli, Davide, Kipf, Thomas
Deep generative models for graphs have shown great promise in the area of drug design, but have so far found little application beyond generating graph-structured molecules. In this work, we demonstrate a proof of concept for the challenging task of
Externí odkaz:
http://arxiv.org/abs/1910.14388
Chest X-rays are one of the most commonly used technologies for medical diagnosis. Many deep learning models have been proposed to improve and automate the abnormality detection task on this type of data. In this paper, we propose a different approac
Externí odkaz:
http://arxiv.org/abs/1812.00964
Generative adversarial networks have been successfully applied to inpainting in natural images. However, the current state-of-the-art models have not yet been widely adopted in the medical imaging domain. In this paper, we investigate the performance
Externí odkaz:
http://arxiv.org/abs/1809.01471