285 citations · 966 across the 40 of their papers we have counts for
7 papers · 1 filter
Improvable Gap Balancing for Multi-Task Learning
Yanqi Dai, Nanyi Fei, Zhiwu Lu
In multi-task learning (MTL), gradient balancing has recently attracted more research interest than loss balancing since it often leads to better performance. However, loss balanci…
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Bing Su, Dazhao Du, Zhao Yang +6
Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive abi…
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding
Jiechao Guan, Zhiwu Lu, Tao Xiang +1
By transferring knowledge learned from seen/previous tasks, meta learning aims to generalize well to unseen/future tasks. Existing meta-learning approaches have shown promising emp…
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning
Jianhong Zhang, Manli Zhang, Zhiwu Lu +2
Existing few-shot learning (FSL) methods assume that there exist sufficient training samples from source classes for knowledge transfer to target classes with few training samples.…
Meta-Learning across Meta-Tasks for Few-Shot Learning
Nanyi Fei, Zhiwu Lu, Yizhao Gao +3
Existing meta-learning based few-shot learning (FSL) methods typically adopt an episodic training strategy whereby each episode contains a meta-task. Across episodes, these tasks a…
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Jiechao Guan, Zhiwu Lu, Tao Xiang +1
To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes. A major challenge for FSL is that the distributio…
RUM: network Representation learning throUgh Multi-level structural information preservation
Yanlei Yu, Zhiwu Lu, Jiajun Liu +3
We have witnessed the discovery of many techniques for network representation learning in recent years, ranging from encoding the context in random walks to embedding the lower ord…