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Neural Information Processing

29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part I

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Mohammad Tanveer

    1. Indian Institute of Technology Indore, Indore, India

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  2. Sonali Agarwal

    1. Indian Institute of Information Technology - Allahabad, Prayagraj, India

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  3. Seiichi Ozawa

    1. Kobe University, Kobe, Japan

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  4. Asif Ekbal

    1. Indian Institute of Technology Patna, Patna, India

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  5. Adam Jatowt

    1. University of Innsbruck, Innsbruck, Austria

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13623)

Included in the following conference series:

Conference proceedings info: ICONIP 2022.

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About this book

The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.


The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.


The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.






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Keywords

Table of contents (51 papers)

  1. Front Matter

    Pages i-xxxv
    Download chapter PDF

  2. Theory and Algorithms

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Solving Partial Differential Equations Using Point-Based Neural Networks

      • Ning Hua, Wenlian Lu
      Pages 3-14

    3. Patch Mix Augmentation with Dual Encoders for Meta-Learning

      • Hong Yu, Fanzhang Li
      Pages 15-26

    4. Tacit Commitments Emergence in Multi-agent Reinforcement Learning

      • Boyin Liu, Zhiqiang Pu, Junlong Gao, Jianqiang Yi, Zhenyu Guo
      Pages 27-36

    5. Saccade Direction Information Channel

      • Qiaohong Hao, Mateu Sbert, Miquel Feixas, Yi Zhang, Marius Vila, Jiawan Zhang
      Pages 37-50

    6. Shared-Attribute Multi-Graph Clustering with Global Self-Attention

      • Jianpeng Chen, Zhimeng Yang, Jingyu Pu, Yazhou Ren, Xiaorong Pu, Li Gao et al.
      Pages 51-63

    7. Mutual Diverse-Label Adversarial Training

      • Mingzhe Li, Sizhe Chen, Xiaolin Huang
      Pages 64-75

    8. Multi-Agent Hyper-Attention Policy Optimization

      • Bin Zhang, Zhiwei Xu, Yiqun Chen, Dapeng Li, Yunpeng Bai, Guoliang Fan et al.
      Pages 76-87

    9. Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks

      • Kang Song, Wangshu Yao, Xuan Zhu
      Pages 88-99

    10. FPD: Feature Pyramid Knowledge Distillation

      • Qi Wang, Lu Liu, Wenxin Yu, Zhiqiang Zhang, Yuxin Liu, Shiyu Cheng et al.
      Pages 100-111

    11. An Effective Ensemble Model Related to Incremental Learning in Neural Machine Translation

      • Pumeng Shi
      Pages 112-127

    12. Local-Global Semantic Fusion Single-shot Classification Method

      • Jianwei Cai, Kun Fang, Weihao Yu, Jie Yang, Yu Qiao
      Pages 128-139

    13. Self-Reinforcing Feedback Domain Adaptation Channel

      • Yan Jia, Xiang Zhang, Long Lan, Zhigang Luo
      Pages 140-152

    14. General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data

      • Masahiro Kohjima, Yuta Nambu, Yuki Kurauchi, Ryuji Yamamoto
      Pages 153-164

    15. Additional Learning for Joint Probability Distribution Matching in BiGAN

      • Jiazhou Zheng, Hiroaki Aizawa, Takio Kurita
      Pages 165-176

    16. Multi-view Self-attention for Regression Domain Adaptation with Feature Selection

      • Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel, Thomas Ranvier, Eric Michoux
      Pages 177-188

    17. EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields

      • Zhiyuan Yang, Qingfu Zhang
      Pages 189-199

    18. Partial Label Learning with Gradually Induced Error-Correction Output Codes

      • Yu-Xuan Shi, Deng-Bao Wang, Min-Ling Zhang
      Pages 200-211

    19. HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-Based Optimizer

      • Omatharv Bharat Vaidya, Rithvik Terence DSouza, Soma Dhavala, Snehanshu Saha, Swagatam Das
      Pages 212-223
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Other volumes

  1. Neural Information Processing

  2. Neural Information Processing

  3. Neural Information Processing

  4. Neural Information Processing

  5. Neural Information Processing

  6. Neural Information Processing

  7. Neural Information Processing

Editors and Affiliations

  • Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  • Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  • Kobe University, Kobe, Japan

    Seiichi Ozawa

  • Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  • University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

Bibliographic Information

  • Book Title: Neural Information Processing

  • Book Subtitle: 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part I

  • Editors: Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-30105-6

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Softcover ISBN: 978-3-031-30104-9Published: 13 April 2023

  • eBook ISBN: 978-3-031-30105-6Published: 12 April 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXXV, 627

  • Number of Illustrations: 10 b/w illustrations, 162 illustrations in colour

  • Topics: Pattern Recognition, Data Mining and Knowledge Discovery, Machine Learning, Computer Applications

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