Search Results for author: Xiaotian Gao

Found 7 papers, 3 papers with code

NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition

no code implementations20 Feb 2023 Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs).

LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data

no code implementations19 Jun 2022 Wenlei Shi, Xinquan Huang, Xiaotian Gao, Xinran Wei, Jia Zhang, Jiang Bian, Mao Yang, Tie-Yan Liu

Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE).

Learning Physics-Informed Neural Networks without Stacked Back-propagation

1 code implementation18 Feb 2022 Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, LiWei Wang, Tie-Yan Liu

In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks.

SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation

1 code implementation ICLR 2022 Cong Guo, Yuxian Qiu, Jingwen Leng, Xiaotian Gao, Chen Zhang, Yunxin Liu, Fan Yang, Yuhao Zhu, Minyi Guo

This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements.

Data Free Quantization

OpEvo: An Evolutionary Method for Tensor Operator Optimization

no code implementations10 Jun 2020 Xiaotian Gao, Cui Wei, Lintao Zhang, Mao Yang

Training and inference efficiency of deep neural networks highly rely on the performance of tensor operators on hardware platforms.

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