24 citations · 51 across the 8 of their papers we have counts for
8 papers
Alignment Attention by Matching Key and Query Distributions
Shujian Zhang, Xinjie Fan, Huangjie Zheng +2
The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains. Most such models use multi-head self-atte…
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng +4
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space. To avoid the sampl…
Bayesian Attention Belief Networks
Shujian Zhang, Xinjie Fan, Bo Chen +1
Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less exp…
Adversarially Adaptive Normalization for Single Domain Generalization
Xinjie Fan, Qifei Wang, Junjie Ke +3
Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversaria…
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Xinjie Fan, Shujian Zhang, Korawat Tanwisuth +2
Dropout has been demonstrated as a simple and effective module to not only regularize the training process of deep neural networks, but also provide the uncertainty estimation for…
Bayesian Attention Modules
Xinjie Fan, Shujian Zhang, Bo Chen +1
Attention modules, as simple and effective tools, have not only enabled deep neural networks to achieve state-of-the-art results in many domains, but also enhanced their interpreta…
Attention that does not Explain Away
Nan Ding, Xinjie Fan, Zhenzhong Lan +2
Models based on the Transformer architecture have achieved better accuracy than the ones based on competing architectures for a large set of tasks. A unique feature of the Transfor…
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Xinjie Fan, Yizhe Zhang, Zhendong Wang +1
Sequence generation models are commonly refined with reinforcement learning over user-defined metrics. However, high gradient variance hinders the practical use of this method. To…