WI-TMLEGA: Weight Initialization and Training Method Based on Entropy Gain and Learning Rate Adjustment.

Hongchuan Tang, Zhongguo Li, Qi Wang, Wenbin Fan

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Hongchuan TangSchool of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Zhongguo LiSchool of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Qi WangSchool of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Wenbin FanJiangsu JBPV Intelligent Equipment Co., Ltd., Zhangjiagang 215634, China.

Funding

the Doctoral Scientific ResearchStart-up Fund Project of NanTong Institute of Technology 2023XK(B)02the Key Research andDevelopment Program of Jiangsu Province BE2022062the Zhangjiagang Science and TechnologyPlanning Project ZKYY2314

6 · The paper itself

Abstract

Addressing the issues of prolonged training times and low recognition rates in large model applications, this paper proposes a weight training method based on entropy gain for weight initialization and dynamic adjustment of the learning rate using the multilayer perceptron (MLP) model as an example. Initially, entropy gain was used to replace random initial values for weight initialization. Subsequently, an incremental learning rate strategy was employed for weight updates. The model was trained and validated using the MNIST handwritten digit dataset. The experimental results showed that, compared to random initialization, the proposed initialization method improves training effectiveness by 39.8% and increases the maximum recognition accuracy by 8.9%, demonstrating the feasibility of this method in large model applications.

Indexed as

learning rateMNIST datasetmultilayer perceptronweight initializationweight update

Identifiers

PMID39202084

DOI10.3390/e26080614

PMCPMC11353430

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.