55 citations · 73 across the 8 of their papers we have counts for
8 papers
DPCIPI: A pre-trained deep learning model for predicting cross-immunity between drifted strains of Influenza A/H3N2
Yiming Du, Zhuotian Li, Qian He +7
Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development. Traditional neural network methods, such as BiLSTM, could be ineffe…
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Dandan Guo, Zhuo Li, Meixi Zheng +3
Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training s…
Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases
Zhihao Yuan, Xu Yan, Zhuo Li +4
Recent progress in 3D scene understanding has explored visual grounding (3DVG) to localize a target object through a language description. However, existing methods only consider t…
Graph Enhanced Contrastive Learning for Radiology Findings Summarization
Jinpeng Hu, Zhuo Li, Zhihong Chen +3
The impression section of a radiology report summarizes the most prominent observation from the findings section and is the most important section for radiologists to communicate t…
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
Jian Peng, Bo Tang, Hao Jiang +4
Artificial neural networks face the well-known problem of catastrophic forgetting. What's worse, the degradation of previously learned skills becomes more severe as the task sequen…
Understanding the Importance of Single Directions via Representative Substitution
Li Chen, Hailun Ding, Qi Li +3
Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior. The interpretation of individual units, which are neurons in MLPs or…
Understanding the Importance of Single Directions via Representative Substitution
Li Chen, Hailun Ding, Qi Li +3
Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior. The interpretation of individual units, which are neurons in MLPs or…
Overcoming Catastrophic Forgetting by Soft Parameter Pruning
Jian Peng, Jiang Hao, Zhuo Li +5
Catastrophic forgetting is a challenge issue in continual learning when a deep neural network forgets the knowledge acquired from the former task after learning on subsequent tasks…