no code implementations • 24 Nov 2022 • Siyi Li, Mingrui Zhang, Matthew D. Piggott
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms.
no code implementations • 22 Jul 2022 • Joseph G. Wallwork, Jingyi Lu, Mingrui Zhang, Matthew D. Piggott
We demonstrate that this approach is able to obtain the same accuracy with a reduced computational cost, for adaptive mesh test cases related to flow around tidal turbines, which interact via their downstream wakes, and where the overall power output of the farm is taken as the QoI.
1 code implementation • 21 Jun 2022 • Mingrui Zhang, Jianhong Wang, James Tlhomole, Matthew D. Piggott
General optical flow methods are typically designed for rigid body motion, and thus struggle if applied to fluid motion estimation directly.
1 code implementation • 6 Jun 2022 • Yu Fang, Jiancheng Liu, Mingrui Zhang, Jiasheng Zhang, Yidong Ma, Minchen Li, Yuanming Hu, Chenfanfu Jiang, Tiantian Liu
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers.
1 code implementation • 24 Apr 2022 • Wenbin Song, Mingrui Zhang, Joseph G. Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew D. Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen, Jun Wang
However, mesh movement methods, such as the Monge-Ampere method, require the solution of auxiliary equations, which can be extremely expensive especially when the mesh is adapted frequently.
1 code implementation • CVPR 2022 • Jiahao Yu, Li Chen, Mingrui Zhang, Mading Li
While several recent works exploit tree-based algorithm to preserve image content better, all of them resort to hand-crafted adjustment rules to optimize the collage tree structure, leading to the failure of fully exploring the structure space of collage tree.
no code implementations • 19 Oct 2021 • Mingrui Zhang, Mading Li, Li Chen, Jiahao Yu
To overcome the lack of training data, we pretrain our deep aesthetic network on a large scale image aesthetic dataset (CPC) for general aesthetic feature extraction and propose an attention fusion module for structural collage feature representation.
no code implementations • 7 May 2021 • Mingrui Zhang
As a projection-free algorithm, Frank-Wolfe (FW) method, also known as conditional gradient, has recently received considerable attention in the machine learning community.
1 code implementation • 28 Jul 2020 • Mingrui Zhang, Matthew D. Piggott
Recently, the development of deep learning based methods has inspired new approaches to tackle the PIV problem.
no code implementations • ICML 2020 • Lin Chen, Yifei Min, Mingrui Zhang, Amin Karbasi
Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the data, but result in serious and potentially dangerous prediction errors.
no code implementations • NeurIPS 2019 • Mingrui Zhang, Lin Chen, Hamed Hassani, Amin Karbasi
In this paper, we propose three online algorithms for submodular maximisation.
no code implementations • 10 Oct 2019 • Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi
One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wide-spread use in many machine learning applications.
no code implementations • 16 Sep 2019 • Yumeng Zhang, Gaoguo Jia, Li Chen, Mingrui Zhang, Junhai Yong
The dynamic image compresses the motion information of video into a still image, removing the interference factors such as the background.
no code implementations • 17 Feb 2019 • Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi
How can we efficiently mitigate the overhead of gradient communications in distributed optimization?
no code implementations • 28 Jan 2019 • Lin Chen, Mingrui Zhang, Hamed Hassani, Amin Karbasi
In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided.
no code implementations • 18 May 2018 • Lin Chen, Mingrui Zhang, Amin Karbasi
In this paper, we propose the first computationally efficient projection-free algorithm for bandit convex optimization (BCO).