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Found 6 papers, 2 papers with code
Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations
Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning.
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations
Neural networks, especially the recent proposed neural operator models, are increasingly being used to find the solution operator of differential equations.
VI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations
Although physics-informed neural networks(PINNs) have progressed a lot in many real applications recently, there remains problems to be further studied, such as achieving more accurate results, taking less training time, and quantifying the uncertainty of the predicted results.
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation
Recent cross-lingual cross-modal works attempt to extend Vision-Language Pre-training (VLP) models to non-English inputs and achieve impressive performance.
Ranked #1 on
Multimodal Machine Translation
on Multi30K
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality.
Ranked #1 on
Image Retrieval
on AIC-ICC
