4 citations · 9 across the 6 of their papers we have counts for
7 papers
EcoSpa: Efficient Transformer Training with Coupled Sparsity
Jinqi Xiao, Cheng Luo, Lingyi Huang +8
Transformers have become the backbone of modern AI, yet their high computational demands pose critical system challenges. While sparse training offers efficiency gains, existing me…
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Jinqi Xiao, Miao Yin, Yu Gong +3
Attention-based vision models, such as Vision Transformer (ViT) and its variants, have shown promising performance in various computer vision tasks. However, these emerging archite…
GUAP: Graph Universal Attack Through Adversarial Patching
Xiao Zang, Jie Chen, Bo Yuan
Graph neural networks (GNNs) are a class of effective deep learning models for node classification tasks; yet their predictive capability may be severely compromised under adversar…
Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner
Xiao Zang, Miao Yin, Lingyi Huang +3
Neural network (NN)-based methods have emerged as an attractive approach for robot motion planning due to strong learning capabilities of NN models and their inherently high parall…
Noise Injection-based Regularization for Point Cloud Processing
Xiao Zang, Yi Xie, Siyu Liao +2
Noise injection-based regularization, such as Dropout, has been widely used in image domain to improve the performance of deep neural networks (DNNs). However, efficient regulariza…
Elastic -means clustering of functional data for posterior exploration, with an application to inference on acute respiratory infection dynamics
Xiao Zang, Sebastian Kurtek, Oksana Chkrebtii +1
We propose a new method for clustering of functional data using a -means framework. We work within the elastic functional data analysis framework, which allows for decomposition…
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Xiao Zang, Yi Xie, Jie Chen +1
Deep neural networks, while generalize well, are known to be sensitive to small adversarial perturbations. This phenomenon poses severe security threat and calls for in-depth inves…