Zobrazeno 1 - 10
of 503 920
pro vyhledávání: '"Computer Science - Artificial Intelligence"'
Autor:
Zeng, Qian, Zhang, Fan
Glaucoma is a leading cause of irreversible blindness worldwide. While deep learning approaches using fundus images have largely improved early diagnosis of glaucoma, variations in images from different devices and locations (known as domain shifts)
Externí odkaz:
http://arxiv.org/abs/2407.04396
Due to the effective performance of multi-scale feature fusion, Path Aggregation FPN (PAFPN) is widely employed in YOLO detectors. However, it cannot efficiently and adaptively integrate high-level semantic information with low-level spatial informat
Externí odkaz:
http://arxiv.org/abs/2407.04381
Quantum machine learning models based on parametrized quantum circuits, also called quantum neural networks (QNNs), are considered to be among the most promising candidates for applications on near-term quantum devices. Here we explore the expressivi
Externí odkaz:
http://arxiv.org/abs/2407.04371
Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electr
Externí odkaz:
http://arxiv.org/abs/2407.04377
Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input. Within our approach, robust featur
Externí odkaz:
http://arxiv.org/abs/2407.04370
Autor:
Anokhin, Petr, Semenov, Nikita, Sorokin, Artyom, Evseev, Dmitry, Burtsev, Mikhail, Burnaev, Evgeny
Advancements in generative AI have broadened the potential applications of Large Language Models (LLMs) in the development of autonomous agents. Achieving true autonomy requires accumulating and updating knowledge gained from interactions with the en
Externí odkaz:
http://arxiv.org/abs/2407.04363
As autonomous driving systems (ADS) advance towards higher levels of autonomy, orchestrating their safety verification becomes increasingly intricate. This paper unveils ScenarioFuzz, a pioneering scenario-based fuzz testing methodology. Designed lik
Externí odkaz:
http://arxiv.org/abs/2407.04359
Automatically generating symbolic music-music scores tailored to specific human needs-can be highly beneficial for musicians and enthusiasts. Recent studies have shown promising results using extensive datasets and advanced transformer architectures.
Externí odkaz:
http://arxiv.org/abs/2407.04331
High-speed railway (HSR) communications are pivotal for ensuring rail safety, operations, maintenance, and delivering passenger information services. The high speed of trains creates rapidly time-varying wireless channels, increases the signaling ove
Externí odkaz:
http://arxiv.org/abs/2407.04336
Autor:
Kumar, Mohit, Valentinitsch, Alexander, Fuchs, Magdalena, Brucker, Mathias, Bowles, Juliana, Husakovic, Adnan, Abbas, Ali, Moser, Bernhard A.
This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generalisation and approximation errors, and sample complexity. For classifica
Externí odkaz:
http://arxiv.org/abs/2407.04335