Search Results for author: Qiang Niu

Found 7 papers, 2 papers with code

Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation

1 code implementation16 Apr 2023 Yunjie Ji, Yan Gong, Yong Deng, Yiping Peng, Qiang Niu, Baochang Ma, Xiangang Li

Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models.

Instruction Following

Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases

1 code implementation26 Mar 2023 Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Lei Zhang, Baochang Ma, Xiangang Li

However current research rarely studies the impact of different amounts of instruction data on model performance, especially in the real-world use cases.

Optimal Settings for Cryptocurrency Trading Pairs

no code implementations20 Oct 2022 Di Zhang, Qiang Niu, Youzhou Zhou

2) It satisfies the connectivity constraint, that is, all currencies are guaranteed to be tradable.


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

Modeling Randomly Walking Volatility with Chained Gamma Distributions

no code implementations4 Jul 2022 Di Zhang, Qiang Niu, Youzhou Zhou

2) If the variational inference(VI) is used for state estimation, it runs much faster than Monte Carlo(MC) methods since the calculation of the posterior uses only basic arithmetic operations.

Time Series Analysis Variational Inference

Sub-GMN: The Neural Subgraph Matching Network Model

no code implementations1 Apr 2021 Zixun Lan, Limin Yu, Linglong Yuan, Zili Wu, Qiang Niu, Fei Ma

Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage.

Graph Representation Learning Information Retrieval +2

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

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