65 citations · 297 across the 16 of their papers we have counts for
9 papers · 1 filter
Group Equivariant Fourier Neural Operators for Partial Differential Equations
Jacob Helwig, Xuan Zhang, Cong Fu +3
We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain. Since the laws of physics do not depend on th…
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization
Weidong Cao, Mouhacine Benosman, Xuan Zhang +1
The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit…
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning
Weidong Cao, Mouhacine Benosman, Xuan Zhang +1
The design automation of analog circuits is a longstanding challenge in the integrated circuit field. This paper presents a deep reinforcement learning method to expedite the desig…
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Zhao Xu, Youzhi Luo, Xuan Zhang +7
Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies…
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
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…
Disentangled Recurrent Wasserstein Autoencoder
Jun Han, Martin Renqiang Min, Ligong Han +2
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as i…
Attacking Vision-based Perception in End-to-End Autonomous Driving Models
Adith Boloor, Karthik Garimella, Xin He +3
Recent advances in machine learning, especially techniques such as deep neural networks, are enabling a range of emerging applications. One such example is autonomous driving, whic…