Search Results for author: Ming Gu

Found 8 papers, 2 papers with code

Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models

no code implementations ACL 2022 Biru Zhu, Yujia Qin, Fanchao Qi, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu

To validate our viewpoints, we design two methods to evaluate the robustness of FMS: (1) model disguise attack, which post-trains an inferior PTM with a contrastive objective, and (2) evaluation data selection, which selects a subset of the data points for FMS evaluation based on K-means clustering.

Backdoor Attack Model Selection

XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding

no code implementations14 Mar 2022 Zhangxuan Gu, Changhua Meng, Ke Wang, Jun Lan, Weiqiang Wang, Ming Gu, Liqing Zhang

Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings.

Optical Character Recognition

Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins

no code implementations4 Mar 2022 Han Liu, Xiaoyu Song, Ge Gao, Hehua Zhang, Yu-Shen Liu, Ming Gu

Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.

An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis

no code implementations29 Sep 2021 Michael Yeh, Ming Gu

For $m\times n$ matrices where a few principal components explain most of the variance in the data, we develop one such algorithm that runs in $O(mnl)$ time, where $l\ll \min(m, n)$ is a small multiple of the number of principal components.

Dimensionality Reduction

Fast Low-rank Metric Learning for Large-scale and High-dimensional Data

1 code implementation NeurIPS 2019 Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu

Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints.

Metric Learning

Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-rank Matrix Approximations

no code implementations13 Apr 2018 Jianwei Xiao, Ming Gu, Julien Langou

In contrast, randomized QRCP (RQRCP) algorithms have proven themselves empirically to be highly competitive with high-performance implementations of QR in processing time, on uniprocessor and shared memory machines, and as reliable as QRCP in pivot quality.

Numerical Analysis

Low-Rank Matrix Approximations with Flip-Flop Spectrum-Revealing QR Factorization

no code implementations6 Mar 2018 Yuehua Feng, Jianwei Xiao, Ming Gu

We present Flip-Flop Spectrum-Revealing QR (Flip-Flop SRQR) factorization, a significantly faster and more reliable variant of the QLP factorization of Stewart, for low-rank matrix approximations.

Numerical Analysis Numerical Analysis 15A18, 15A23, 65F99

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