44 citations · 118 across the 9 of their papers we have counts for
9 papers
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Minghao Guo, Veronika Thost, Samuel W Song +4
The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the…
Data-Efficient Graph Grammar Learning for Molecular Generation
Minghao Guo, Veronika Thost, Beichen Li +3
The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets…
Polygrammar: Grammar for Digital Polymer Representation and Generation
Minghao Guo, Wan Shou, Liane Makatura +3
Polymers are widely-studied materials with diverse properties and applications determined by different molecular structures. It is essential to represent these structures clearly a…
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…
Texture Memory-Augmented Deep Patch-Based Image Inpainting
Rui Xu, Minghao Guo, Jiaqi Wang +3
Patch-based methods and deep networks have been employed to tackle image inpainting problem, with their own strengths and weaknesses. Patch-based methods are capable of restoring a…
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
Minghao Guo, Yuzhe Yang, Rui Xu +2
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustne…
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…