Search Results for author: Shengxin Zhu

Found 6 papers, 0 papers with code

Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review

no code implementations23 Oct 2023 Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu

This survey elucidates foundational principles of prompt engineering, such as role-prompting, one-shot, and few-shot prompting, as well as more advanced methodologies such as the chain-of-thought and tree-of-thoughts prompting.

Prompt Engineering

Learning with linear mixed model for group recommendation systems

no code implementations17 Dec 2022 Baode Gao, Guangpeng Zhan, Hanzhang Wang, Yiming Wang, Shengxin Zhu

Accurate prediction of users' responses to items is one of the main aims of many computational advising applications.

Recommendation Systems

Dynamical softassign and adaptive parameter tuning for graph matching

no code implementations17 Aug 2022 Binrui Shen, Qiang Niu, Shengxin Zhu

Combining the adaptive step size and the dynamical softassign, we propose a novel graph matching algorithm: the adaptive projected fixed-point method with dynamical softassign.

Graph Matching

Generalized Rough Polyharmonic Splines for Multiscale PDEs with Rough Coefficients

no code implementations2 Mar 2021 Xinliang Liu, Lei Zhang, Shengxin Zhu

In this paper, we demonstrate the construction of generalized Rough Polyhamronic Splines (GRPS) within the Bayesian framework, in particular, for multiscale PDEs with rough coefficients.

Numerical Analysis Numerical Analysis

Fabricated Pictures Detection with Graph Matching

no code implementations16 Jan 2020 Binrui Shen, Qiang Niu, Shengxin Zhu

Fabricating experimental pictures in research work is a serious academic misconduct, which should better be detected in the reviewing process.

Graph Matching

Sparse inversion for derivative of log determinant

no code implementations2 Nov 2019 Shengxin Zhu, Andrew J Wathen

Algorithms for Gaussian process, marginal likelihood methods or restricted maximum likelihood methods often require derivatives of log determinant terms.

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