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IEEE Transactions on Neural Networks and Learning Systems Template

Write in a clean editor, then format for IEEE Transactions on Neural Networks and Learning Systems in one click — DocuGuru applies the official IEEE template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.

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About the IEEE Transactions on Neural Networks and Learning Systems format

IEEE Transactions on Neural Networks and Learning Systems is a peer-reviewed journal published by IEEE, covering Neural Networks and Applications, Neural Networks Stability and Synchronization, Face and Expression Recognition. Over its lifetime it has published 8,966 papers, which have received 442,589 citations.

PublisherIEEE
Reference styleNumbered (IEEE)
Numbered — [1], [2] in the text
[1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Neural Networks and Learning Systems, vol. 12, no. 3, pp. 45–58, 2023.

Formats any DOI in IEEE Transactions on Neural Networks and Learning Systems style. No sign-up.

Abbreviation (ISO-4)TNNLS
Publishes research inNeural Networks and Applications Neural Networks Stability and Synchronization Face and Expression Recognition Domain Adaptation and Few-Shot Learning Advanced Memory and Neural Computing
ISSN2162-237X
Citation impact (2-yr)7.8
h-index242
i10-index5,722
Top institutions publishing hereChinese Academy of Sciences
Journal websitecis.ieee.org
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Papers published in IEEE Transactions on Neural Networks and Learning Systems per year

352
2014
409
2015
359
2016
395
2017
438
2018
506
2019
722
2020
1.1K
2021
1.1K
2022
1K
2023
842
2024
808
2025

Citation impact of IEEE Transactions on Neural Networks and Learning Systems by publication year

27.3K
2014
33.2K
2015
36.5K
2016
30.3K
2017
33.6K
2018
39.3K
2019
55.5K
2020
58K
2021
43.6K
2022
26K
2023
11.5K
2024
3K
2025

Citations each year’s papers have accumulated so far — the most recent years are still building up.

Most-cited papers in IEEE Transactions on Neural Networks and Learning Systems

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu, Shirui Pan, Fengwen Chen et al. · 24 Mar 2020

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented…

9,424 citations Cite SaveGo to paper →
LSTM: A Search Space Odyssey

Klaus Greff, Rupesh K. Srivastava, Jan Koutník et al. · 14 Jul 2016

Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components…

6,899 citations Cite SaveGo to paper →
Object Detection With Deep Learning: A Review

Zhong‐Qiu Zhao, Peng Zheng, Shou-Tao Xu et al. · 29 Jan 2019

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and…

5,392 citations Cite SaveGo to paper →
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Zewen Li, Fan Liu, Wenjie Yang et al. · 10 Jun 2021

A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus…

4,920 citations Cite SaveGo to paper →
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Shaoxiong Ji, Shirui Pan, Erik Cambria et al. · 26 Apr 2021

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and…

2,745 citations Cite SaveGo to paper →

IEEE Transactions on Neural Networks and Learning Systems template — frequently asked questions

How do I write a paper in the IEEE Transactions on Neural Networks and Learning Systems format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the IEEE Transactions on Neural Networks and Learning Systems template. When you export, DocuGuru compiles the paper into the official IEEE format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does IEEE Transactions on Neural Networks and Learning Systems use?
IEEE Transactions on Neural Networks and Learning Systems uses Numbered (IEEE) references, shown as numbered [1], [2] markers in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Neural Networks and Learning Systems, vol. 12, no. 3, pp. 45–58, 2023.
Do I need to know LaTeX to submit to IEEE Transactions on Neural Networks and Learning Systems?
No. DocuGuru generates the IEEEtran LaTeX class and compiles the PDF for you in the background, so you get a IEEE-ready IEEE Transactions on Neural Networks and Learning Systems document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
Can I import an existing draft into the IEEE Transactions on Neural Networks and Learning Systems template?
Yes. Paste or upload your current manuscript — Word, LaTeX, Markdown, or plain text — and DocuGuru reflows it into the IEEE Transactions on Neural Networks and Learning Systems format with correct headings, figures, tables, and numbered citations.
Who publishes IEEE Transactions on Neural Networks and Learning Systems?
IEEE Transactions on Neural Networks and Learning Systems is a engineering and computer science journal published by IEEE. DocuGuru's IEEE Transactions on Neural Networks and Learning Systems template matches IEEE's official submission format.
Can I export a submission-ready IEEE Transactions on Neural Networks and Learning Systems PDF?
Yes — DocuGuru produces a PDF built with the official IEEE Transactions on Neural Networks and Learning Systems template (the IEEEtran class) that is ready to submit to IEEE, together with the matching LaTeX source files.
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