36 citations · 63 across the 8 of their papers we have counts for
8 papers
One-Shot Price Forecasting with Covariate-Guided Experts under Privacy Constraints
Ren He, Yinliang Xu, Jinfeng Wang +2
Forecasting in power systems often involves multivariate time series with complex dependencies and strict privacy constraints across regions. Traditional forecasting methods requir…
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Zhongkai Hao, Chang Su, Songming Liu +6
Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings. However, it is largely in its infancy due to the…
Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image
Yuanzheng Ma, Xinyue Wang, Benqi Zhao +4
Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early de…
GNOT: A General Neural Operator Transformer for Operator Learning
Zhongkai Hao, Zhengyi Wang, Hang Su +6
Learning partial differential equations' (PDEs) solution operators is an essential problem in machine learning. However, there are several challenges for learning operators in prac…
Deep learning for full-field ultrasonic characterization
Yang Xu, Fatemeh Pourahmadian, Jian Song +1
This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform for distributed reconstruction of mechanical properties in lay…
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
Jian Song, Di Liang, Rumei Li +5
Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown general benefits in…
DABERT: Dual Attention Enhanced BERT for Semantic Matching
Sirui Wang, Di Liang, Jian Song +2
Transformer-based pre-trained language models such as BERT have achieved remarkable results in Semantic Sentence Matching. However, existing models still suffer from insufficient a…
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
Zhongkai Hao, Chengyang Ying, Hang Su +3
Deep learning based approaches like Physics-informed neural networks (PINNs) and DeepONets have shown promise on solving PDE constrained optimization (PDECO) problems. However, exi…