18 citations · 20 across the 2 of their papers we have counts for
4 papers · 1 filter
Towards Evaluating and Training Verifiably Robust Neural Networks
Zhaoyang Lyu, Minghao Guo, Tong Wu +3
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP train…
AM-LFS: AutoML for Loss Function Search
Chuming Li, Yuan Xin, Chen Lin +4
Designing an effective loss function plays an important role in visual analysis. Most existing loss function designs rely on hand-crafted heuristics that require domain experts to…
Online Hyper-parameter Learning for Auto-Augmentation Strategy
Chen Lin, Minghao Guo, Chuming Li +5
Data augmentation is critical to the success of modern deep learning techniques. In this paper, we propose Online Hyper-parameter Learning for Auto-Augmentation (OHL-Auto-Aug), an…
IRLAS: Inverse Reinforcement Learning for Architecture Search
Minghao Guo, Zhao Zhong, Wei Wu +2
In this paper, we propose an inverse reinforcement learning method for architecture search (IRLAS), which trains an agent to learn to search network structures that are topological…