9 citations · 47 across the 29 of their papers we have counts for
14 papers · 1 filter
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
Khondoker Murad Hossain, Tim Oates
As deep neural networks and the datasets used to train them get larger, the default approach to integrating them into research and commercial projects is to download a pre-trained…
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
Zhuqing Liu, Xin Zhang, Songtao Lu +1
Recently, min-max optimization problems have received increasing attention due to their wide range of applications in machine learning (ML). However, most existing min-max solution…
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Shuai Zhang, Meng Wang, Pin-Yu Chen +3
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and sto…
ASGNN: Graph Neural Networks with Adaptive Structure
Zepeng Zhang, Songtao Lu, Zengfeng Huang +1
The graph neural network (GNN) models have presented impressive achievements in numerous machine learning tasks. However, many existing GNN models are shown to be vulnerable to adv…
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks
Zhuqing Liu, Xin Zhang, Prashant Khanduri +2
In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in mo…
Understanding Benign Overfitting in Gradient-Based Meta Learning
Lisha Chen, Songtao Lu, Tianyi Chen
Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conve…