5 citations · 9 across the 5 of their papers we have counts for
5 papers
Continual Learning on Graphs: Challenges, Solutions, and Opportunities
Xikun Zhang, Dongjin Song, Dacheng Tao
Continual learning on graph data has recently attracted paramount attention for its aim to resolve the catastrophic forgetting problem on existing tasks while adapting the sequenti…
Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects
Jingcai Guo, Zhijie Rao, Zhi Chen +2
Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, i.e., fine-grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned visu…
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation
Changtong Zan, Liang Ding, Li Shen +4
Zero-shot translation (ZST), which is generally based on a multilingual neural machine translation model, aims to translate between unseen language pairs in training data. The comm…
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup
Yan Sun, Li Shen, Hao Sun +2
Adaptive optimization has achieved notable success for distributed learning while extending adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, includin…
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Shiye Lei, Zhuozhuo Tu, Leszek Rutkowski +4
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large n…