TSFool: Crafting High-quality Adversarial Time Series through Multi-objective Optimization to Fool Recurrent Neural Network Classifiers

1 code implementation • 14 Sep 2022 • Yanyun Wang, Dehui Du, YuanHao Liu

In this paper, an efficient and widely applicable approach called TSFool for crafting high-quality adversarial time series for the RNN classifier is proposed.

Adversarial Attack Time Series Classification

Meta Pattern Concern Score: A Novel Metric for Customizable Evaluation of Multi-classification

1 code implementation • 14 Sep 2022 • Yanyun Wang, Dehui Du, YuanHao Liu

Classifiers have been widely implemented in practice, while how to evaluate them properly remains a problem.

Attribute-Based Robotic Grasping with One-Grasp Adaptation

no code implementations • 6 Apr 2021 • Yang Yang, YuanHao Liu, Hengyue Liang, Xibai Lou, Changhyun Choi

In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability.

Robotic Grasping

Towards Powerful Graph Neural Networks: Diversity Matters

no code implementations • 1 Jan 2021 • Xu Bingbing, HuaWei Shen, Qi Cao, YuanHao Liu, Keting Cen, Xueqi Cheng

For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.

Graph Representation Learning Node Classification