28 citations · 96 across the 21 of their papers we have counts for
7 papers · 1 filter
Localized Adversarial Domain Generalization
Wei Zhu, Le Lu, Jing Xiao +3
Deep learning methods can struggle to handle domain shifts not seen in training data, which can cause them to not generalize well to unseen domains. This has led to research attent…
Structure-preserving GANs
Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet +1
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-player game between a generator and a discriminator, can generally be formulated as…
Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting
Wei Zhu, Jiebo Luo, Andrew White
Chemistry research has both high material and computational costs to conduct experiments. Institutions thus consider chemical data to be valuable and there have been few efforts to…
Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization
Wei Zhu, Haitian Zheng, Haofu Liao +2
Deep learning algorithms mine knowledge from the training data and thus would likely inherit the dataset's bias information. As a result, the obtained model would generalize poorly…
Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data
Weijian Li, Wei Zhu, E. Ray Dorsey +1
Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the qua…
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Wei Zhu, Qiang Qiu, Robert Calderbank +2
Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when deali…
Deep Neural Nets with Interpolating Function as Output Activation
Bao Wang, Xiyang Luo, Zhen Li +3
We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function. And we propose end-to-end training and testing algorithms for th…