Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Liang, Gongbo"'
The integration of large language models into public transit systems represents a significant advancement in urban transportation management and passenger experience. This study examines the impact of LLMs within San Antonio's public transit system,
Externí odkaz:
http://arxiv.org/abs/2407.11003
Neural text detectors aim to decide the characteristics that distinguish neural (machine-generated) from human texts. To challenge such detectors, adversarial attacks can alter the statistical characteristics of the generated text, making the detecti
Externí odkaz:
http://arxiv.org/abs/2302.05794
Many natural language related applications involve text generation, created by humans or machines. While in many of those applications machines support humans, yet in few others, (e.g. adversarial machine learning, social bots and trolls) machines tr
Externí odkaz:
http://arxiv.org/abs/2212.11808
The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We p
Externí odkaz:
http://arxiv.org/abs/2203.06288
Autor:
Liang, Gongbo, Alsmadi, Izzat
Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics. The size of such datasets and the complexity of DL models cause such
Externí odkaz:
http://arxiv.org/abs/2202.12831
Most research on domain adaptation has focused on the purely unsupervised setting, where no labeled examples in the target domain are available. However, in many real-world scenarios, a small amount of labeled target data is available and can be used
Externí odkaz:
http://arxiv.org/abs/2110.09641
Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding the co-evolution of galaxies and large-scale structures in modern astron
Externí odkaz:
http://arxiv.org/abs/2012.02368
Autor:
Xing, Xin, Liang, Gongbo, Blanton, Hunter, Rafique, Muhammad Usman, Wang, Chris, Lin, Ai-Ling, Jacobs, Nathan
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3
Externí odkaz:
http://arxiv.org/abs/2012.00119
Autor:
Liang, Gongbo, Greenwell, Connor, Zhang, Yu, Wang, Xiaoqin, Kavuluru, Ramakanth, Jacobs, Nathan
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports, which are often readily available in medical rec
Externí odkaz:
http://arxiv.org/abs/2010.03060
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these works have primarily focused on classification accuracy, ignoring the im
Externí odkaz:
http://arxiv.org/abs/2009.04057