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Found 5 papers, 1 papers with code
Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
In this work, we propose an end-to-end adaptive sampling neural network (MMPDE-Net) based on the moving mesh method, which can adaptively generate new sampling points by solving the moving mesh PDE.
On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem
In practical engineering experiments, the data obtained through detectors are inevitably noisy.
Neural Networks Based on Power Method and Inverse Power Method for Solving Linear Eigenvalue Problems
In this article, we propose two kinds of neural networks inspired by power method and inverse power method to solve linear eigenvalue problems.
Denoising User-aware Memory Network for Recommendation
Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users.
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding
In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods.
