Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sun, Yiming"'
Autor:
Zhu, Zichen, Xu, Yang, Chen, Lu, Yang, Jingkai, Ma, Yichuan, Sun, Yiming, Wen, Hailin, Liu, Jiaqi, Cai, Jinyu, Ma, Yingzi, Zhang, Situo, Zhao, Zihan, Sun, Liangtai, Yu, Kai
Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short con
Externí odkaz:
http://arxiv.org/abs/2402.03173
Autor:
Sun, Yiming, Gao, Yuhe, Bao, Runxue, Cooper, Gregory F., Espino, Jessi, Hochheiser, Harry, Michaels, Marian G., Aronis, John M., Song, Chenxi, Ye, Ye
Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily
Externí odkaz:
http://arxiv.org/abs/2402.01987
The application of transfer learning, leveraging knowledge from source domains to enhance model performance in a target domain, has significantly grown, supporting diverse real-world applications. Its success often relies on shared knowledge between
Externí odkaz:
http://arxiv.org/abs/2310.08459
The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potenti
Externí odkaz:
http://arxiv.org/abs/2306.17257
Deep neural networks are found to be prone to adversarial examples which could deliberately fool the model to make mistakes. Recently, a few of works expand this task from 2D image to 3D point cloud by using global point cloud optimization. However,
Externí odkaz:
http://arxiv.org/abs/2105.09090
Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms. However, the interpretations themselves could contain significant uncert
Externí odkaz:
http://arxiv.org/abs/1904.12991
Autor:
Ahmad Muhammad Aminu, Saminu Mohammad Aliyu, Shamsuddeen Hassan Muhammad, Abdulrasheed Mustapha, Sun Yiming, Saheed Salahudeen Abdullahi, Musa Bello, Abdulkadir Abdullahi
Publikováno v:
2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE).
The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis a