419 citations · 833 across the 13 of their papers we have counts for
13 papers
Learning to Substitute Spans towards Improving Compositional Generalization
Zhaoyi Li, Ying Wei, Defu Lian
Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization. One of the current de-facto solutions…
Wasserstein Distributional Learning
Chengliang Tang, Nathan Lenssen, Ying Wei +1
Learning conditional densities and identifying factors that influence the entire distribution are vital tasks in data-driven applications. Conventional approaches work mostly with…
Learning to generate imaginary tasks for improving generalization in meta-learning
Yichen Wu, Long-Kai Huang, Ying Wei
The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of th…
Meta-learning with an Adaptive Task Scheduler
Huaxiu Yao, Yu Wang, Ying Wei +4
To benefit the learning of a new task, meta-learning has been proposed to transfer a well-generalized meta-model learned from various meta-training tasks. Existing meta-learning al…
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Yifan Zhang, Ying Wei, Qingyao Wu +4
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in real-world applications: 1) only lim…
Improving Generalization in Meta-learning via Task Augmentation
Huaxiu Yao, Longkai Huang, Linjun Zhang +5
Meta-learning has proven to be a powerful paradigm for transferring the knowledge from previous tasks to facilitate the learning of a novel task. Current dominant algorithms train…