|
|
|
|
|
Andreas Nugaard Holm, Dustin Wright and Isabelle Augenstein
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) dropout, which is computationally expensi...
ver más
|
|
|
|
|
|
|
|
|
Shiva Gopal Shrestha and Soni M. Pradhanang
The general practice of rainfall-runoff model development towards physically based and spatially explicit representations of hydrological processes is data-intensive and computationally expensive. Physically based models such as the Soil Water Assessment...
ver más
|
|
|
|
|
|
|
|
|
Xiaoli Yang, Yuxin Xia, Zhenwei Li, Lipei Liu, Zhipeng Fan and Jiayi Zhou
Alzheimer?s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuro...
ver más
|
|
|
|
|
|
|
|
|
Songnan Chen, Mengxia Tang, Ruifang Dong and Jiangming Kan
The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB?D images can provide additional depth information for improving the performance of semanti...
ver más
|
|
|
|
|
|
|
|
|
Xiaohu Zhang and Haifeng Huang
Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and ...
ver más
|
|
|
|