123 citations · 459 across the 16 of their papers we have counts for
33 papers
Fine-grained differentiable physics: a yarn-level model for fabrics
Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt +1
Differentiable physics modeling combines physics models with gradient-based learning to provide model explicability and data efficiency. It has been used to learn dynamics, solve i…
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
Quanshi Zhang, Tian Han, Lixin Fan +5
This is the Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. Deep neural networks (DNNs) have undoubtedly brought grea…
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
Mengzhang Li, Zhanxing Zhu
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads…
Amata: An Annealing Mechanism for Adversarial Training Acceleration
Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou +1
Despite the empirical success in various domains, it has been revealed that deep neural networks are vulnerable to maliciously perturbed input data that much degrade their performa…
Automatic Data Augmentation for 3D Medical Image Segmentation
Ju Xu, Mengzhang Li, Zhanxing Zhu
Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks. It could enrich diversity of training samples that is es…
Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen, Dinghuai Zhang, Michael Gutmann +2
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractab…