activity
20192022
most citedRevisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

13 citations · 28 across the 8 of their papers we have counts for

collaborators

10 papers

cs.LG2022

AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data

Hao Wang, Lixue Liu, Xueguan Song +2

In tunnel boring machine (TBM) underground projects, an accurate description of the rock-soil types distributed in the tunnel can decrease the construction risk ({\it e.g.} surface…

quant-ph20221 cited

Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction

Yang Qian, Yuxuan Du, Dacheng Tao

The variational quantum eigensolver (VQE) is a leading strategy that exploits noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems outperforming classical a…

quant-ph20221 cited

QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines

Zeqiao Zhou, Yuxuan Du, Xinmei Tian +1

The design of fast algorithms for combinatorial optimization greatly contributes to a plethora of domains such as logistics, finance, and chemistry. Quantum approximate optimizatio…

cs.LG202213 cited

Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

Wensheng Li, Chao Zhang, Chuncheng Wang +2

Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of…

cs.CV20224 cited

BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

Zhi Hou, Baosheng Yu, Dacheng Tao

Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribu…

cs.CV20221 cited

ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation

Robin Wang, Yibo Yang, Dacheng Tao

Point cloud classifiers with rotation robustness have been widely discussed in the 3D deep learning community. Most proposed methods either use rotation invariant descriptors as in…