65 citations · 297 across the 16 of their papers we have counts for
16 papers
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…
3D Molecular Geometry Analysis with 2D Graphs
Zhao Xu, Yaochen Xie, Youzhi Luo +7
Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationall…
CamJ: Enabling System-Level Energy Modeling and Architectural Exploration for In-Sensor Visual Computing
Tianrui Ma, Yu Feng, Xuan Zhang +1
CMOS Image Sensors (CIS) are fundamental to emerging visual computing applications. While conventional CIS are purely imaging devices for capturing images, increasingly CIS integra…
Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems
Cong Fu, Xuan Zhang, Huixin Zhang +3
Deep learning methods have been shown to be effective in representing ground-state wave functions of quantum many-body systems. Existing methods use convolutional neural networks (…
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…
LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation
Guoping Xu, Xingrong Wu, Xuan Zhang +1
Medical image segmentation plays an essential role in developing computer-assisted diagnosis and therapy systems, yet still faces many challenges. In the past few years, the popula…
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…
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…
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…
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing
Jinghan Yang, Adith Boloor, Ayan Chakrabarti +2
There is considerable evidence that deep neural networks are vulnerable to adversarial perturbations applied directly to their digital inputs. However, it remains an open question…
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…
Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project
Zhimeng Zhang, Jianan Wu, Xuan Zhang +1
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention. DukeMTMC is a large-scale, well-annotated mul…