Zobrazeno 1 - 10
of 521
pro vyhledávání: '"68txx"'
Autor:
Ennadir, Sofiane, Gandler, Gabriela Zarzar, Cornell, Filip, Cao, Lele, Smirnov, Oleg, Wang, Tianze, Zólyomi, Levente, Brinne, Björn, Asadi, Sahar
Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most research has centered on stati
Externí odkaz:
http://arxiv.org/abs/2412.03783
Autor:
Kumar, Animesh
Publikováno v:
AI-Driven Innovations in Modern Cloud Computing, Computer Science and Engineering, Vol. 14 No. 6, 2024,Oct. 14, 2024
The world has witnessed rapid technological transformation, past couple of decades and with Advent of Cloud computing the landscape evolved exponentially leading to efficient and scalable application development. Now, the past couple of years the dig
Externí odkaz:
http://arxiv.org/abs/2410.15960
Autor:
Kumar, Animesh
Publikováno v:
Transactions on Engineering and Computing Sciences - Vol. 12, No. 4 Publication Date: August 25, 2024
With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem and operations which were followed for decades. The current landscape is where decisions are
Externí odkaz:
http://arxiv.org/abs/2410.15951
Publikováno v:
2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) (pp. 1-6). IEEE
This scientific article presents the implementation of an automated control system for detecting and classifying faults in tuna metal cans using artificial vision. The system utilizes a conveyor belt and a camera for visual recognition triggered by a
Externí odkaz:
http://arxiv.org/abs/2410.17275
Autor:
Jagatheesaperumal, Senthil Kumar, Rahouti, Mohamed, Alfatemi, Ali, Ghani, Nasir, Quy, Vu Khanh, Chehri, Abdellah
Publikováno v:
IEEE Internet of Things Magazine, Year: 2024, Volume: 7, Issue: 5
Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data priva
Externí odkaz:
http://arxiv.org/abs/2409.02127
Autor:
Qiu, Liangdong, Yu, Chengxing, Li, Yanran, Wang, Zhao, Huang, Haibin, Ma, Chongyang, Zhang, Di, Wan, Pengfei, Han, Xiaoguang
Although humans have the innate ability to imagine multiple possible actions from videos, it remains an extraordinary challenge for computers due to the intricate camera movements and montages. Most existing motion generation methods predominantly re
Externí odkaz:
http://arxiv.org/abs/2408.06614
Autor:
Lepage, Yves, Couceiro, Miguel
This work presents a formalization of analogy on numbers that relies on generalized means. It is motivated by recent advances in artificial intelligence and applications of machine learning, where the notion of analogy is used to infer results, creat
Externí odkaz:
http://arxiv.org/abs/2407.18770
Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification Tasks
This study introduces novel superior scoring rules called Penalized Brier Score (PBS) and Penalized Logarithmic Loss (PLL) to improve model evaluation for probabilistic classification. Traditional scoring rules like Brier Score and Logarithmic Loss s
Externí odkaz:
http://arxiv.org/abs/2407.17697
The non-stationary nature of real-world Multivariate Time Series (MTS) data presents forecasting models with a formidable challenge of the time-variant distribution of time series, referred to as distribution shift. Existing studies on the distributi
Externí odkaz:
http://arxiv.org/abs/2407.13194
Singing voice conversion (SVC) aims to convert a singer's voice to another singer's from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or d
Externí odkaz:
http://arxiv.org/abs/2407.07728