Zobrazeno 1 - 10
of 3 268
pro vyhledávání: '"adversarial training"'
Autor:
Wai Yan Ryana Fok, Andreas Fieselmann, Christian Huemmer, Ramyar Biniazan, Marcel Beister, Bernhard Geiger, Steffen Kappler, Sylvia Saalfeld
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Deep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an
Externí odkaz:
https://doaj.org/article/0eca2ff17a324248aeab41fb4faa606e
Publikováno v:
Robotic Intelligence and Automation, 2024, Vol. 44, Issue 3, pp. 351-365.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/RIA-08-2023-0109
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-21 (2024)
Abstract Background Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved dr
Externí odkaz:
https://doaj.org/article/6f22110af321478f8be2cb5e6eae81e8
Publikováno v:
High-Speed Railway, Vol 2, Iss 2, Pp 92-100 (2024)
Multisensor data fusion method can improve the accuracy of bearing fault diagnosis, in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most e
Externí odkaz:
https://doaj.org/article/eebe11d2aed64f6fa66a33cc3526abb9
Publikováno v:
Advanced Science, Vol 11, Iss 36, Pp n/a-n/a (2024)
Abstract Cross‐species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross‐species predictive performance being consistently worse than within‐species perf
Externí odkaz:
https://doaj.org/article/e8e5949bd0dd41db8b4a321930fe9d74
Autor:
Nicolás Torres
Publikováno v:
Natural Language Processing Journal, Vol 8, Iss , Pp 100092- (2024)
This research contributes a comprehensive analysis of gender bias within contemporary AI language models, specifically examining iterations of the GPT series, alongside Gemini and Llama. The study offers a systematic investigation, encompassing multi
Externí odkaz:
https://doaj.org/article/1320e011bf4749c8982ecb32d76b4226
Autor:
Lukács Kuslits, András Horváth, Viktor Wesztergom, Ciaran Beggan, Tibor Rubóczki, Ernő Prácser, Lili Czirok, István Bozsó, István Lemperger
Publikováno v:
Earth, Planets and Space, Vol 76, Iss 1, Pp 1-41 (2024)
Abstract Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Pre
Externí odkaz:
https://doaj.org/article/2a0ec0adb2f5479fbe9e21dafa708cc7
Autor:
Shiza Maham, Abdullah Tariq, Muhammad Usman Ghani Khan, Faten S. Alamri, Amjad Rehman, Tanzila Saba
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end fram
Externí odkaz:
https://doaj.org/article/cb4a8d6d06e44e1e81f74a28f3893933
Publikováno v:
Foundations of Computing and Decision Sciences, Vol 49, Iss 1, Pp 21-36 (2024)
Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool sy
Externí odkaz:
https://doaj.org/article/55705007d66f480caaacbe5c6153442e
Publikováno v:
IEEE Access, Vol 12, Pp 175032-175055 (2024)
In the evolving cyber threat landscape, one of the most visible and pernicious challenges is malware activity detection and analysis. Traditional detection and analysis methods face threats of data high-dimensionality, lack of strength against advers
Externí odkaz:
https://doaj.org/article/5840e22565cf49a083a96ae490191949