57 citations · 267 across the 14 of their papers we have counts for
14 papers
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
Yuchao Lin, Keqiang Yan, Youzhi Luo +3
We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such re…
Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Yi Liu, Yuchao Lin +1
We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on…
Learning Hierarchical Protein Representations via Complete 3D Graph Networks
Limei Wang, Haoran Liu, Yi Liu +2
We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depen…
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
Limei Wang, Yi Liu, Yuchao Lin +2
Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either u…
Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence
Hongyang Gao, Yi Liu, Xuan Zhang +1
We study text representation methods using deep models. Current methods, such as word-level embedding and character-level embedding schemes, treat texts as either a sequence of ato…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
Spherical Message Passing for 3D Graph Networks
Yi Liu, Limei Wang, Meng Liu +3
We consider representation learning of 3D molecular graphs in which each atom is associated with a spatial position in 3D. This is an under-explored area of research, and a princip…
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis
Yi Liu, Shuiwang Ji
Computed tomography (CT) imaging is a promising approach to diagnosing the COVID-19. Machine learning methods can be employed to train models from labeled CT images and predict whe…
CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy
Yi Liu, Shuiwang Ji
Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by…
Topology-Aware Graph Pooling Networks
Hongyang Gao, Yi Liu, Shuiwang Ji
Pooling operations have shown to be effective on computer vision and natural language processing tasks. One challenge of performing pooling operations on graph data is the lack of…
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Sina Mohseni, Fan Yang, Shiva Pentyala +6
Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale…
Deep Learning of High-Order Interactions for Protein Interface Prediction
Yi Liu, Hao Yuan, Lei Cai +1
Protein interactions are important in a broad range of biological processes. Traditionally, computational methods have been developed to automatically predict protein interface fro…
Global Pixel Transformers for Virtual Staining of Microscopy Images
Yi Liu, Hao Yuan, Zhengyang Wang +1
Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of differe…