65 citations · 130 across the 11 of their papers we have counts for
11 papers
Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping
Zhuang Xiong, Yang Gao, Feng Liu +1
Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep le…
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
Zekun Li, Wei Zhao, Feng Shi +9
How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the c…
Does deep learning always outperform simple linear regression in optical imaging?
Shuming Jiao, Yang Gao, Jun Feng +2
Deep learning has been extensively applied in many optical imaging applications in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imag…
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
Wei Zhao, Maxime Peyrard, Fei Liu +3
A robust evaluation metric has a profound impact on the development of text generation systems. A desirable metric compares system output against references based on their semantic…
Better Rewards Yield Better Summaries: Learning to Summarise Without References
Florian Böhm, Yang Gao, Christian M. Meyer +3
Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during…
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
Yang Gao, Christian M. Meyer, Mohsen Mesgar +1
Document summarisation can be formulated as a sequential decision-making problem, which can be solved by Reinforcement Learning (RL) algorithms. The predominant RL paradigm for sum…