Connectionists: IJCNN 2025: Call for Special Session and Workshop Proposals

[Thomas Trappenberg]

Submission Deadline: 15 November 2024
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*Call for Special Session and Competition Proposals*
Submission Deadline: 15 November 2024
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The International Joint Conference on Neural Networks, (IJCNN)
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solicits proposals for special sessions within the technical scope of the
conference. Special sessions, to be organized by internationally recognized
experts, aim to bring together researchers in special focused topics.
Cross-fertilization of technical disciplines and newly emerging research
areas are strongly encouraged.
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*Important Dates*
*15 November 2024: Special Session & Competition Proposals*
*15 December 2024: *Tutorial & Workshop Proposals
*15 January 2025:* Regular Paper Submission
*17 March 2025:* Paper Acceptance Notification
*1 May 2025:* Final Camera Ready Paper
*Topics of Interest*
Papers for IJCNN 2025 will be reviewed by experts in the fields and ranked
based on the criteria of originality, significance, quality, and clarity.
Prospective authors are invited to submit complete papers of no more than
eight (8) pages in IEEE two-column conference proceedings format. Authors
should submit their papers in PDF through the online submission system,
which will be available at https://2025.ijcnn.org/
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*Topics of interest include but are not limited to:*

   - AI for Critical Infrastructures
   - AI for Education
   - AI for Neural Engineering
   - Brain-machine Interfaces
   - Cognitive Models
   - Collective & Ensemble Intelligence
   - Computational Neuroscience
   - Dynamic Neural Networks
   - Efficient and Tiny Neural Networks
   - Ethics and Regulation in AI
   - Generative AI Models
   - Graph Neural Networks
   - Interpretable and Explainable AI
   - Large Language Models
   - Large-Scale Neural Networks
   - Mixture of Experts


   - Modular Neural Networks
   - Neural Engineering
   - Neural Network Applications
   - Neural Networks for Sciences
   - Neuromorphic Systems
   - Neurosymbolic AI
   - Perceptual Neural Networks
   - Quantum Neural Networks
   - Reinforcement Learning
   - Representation & Reasoning
   - Reservoir & Echo-State Networks
   - Spiking Neural Networks
   - Theory of Neural Networks
   - Transformer Networks
   - Trustworthy and Reliable AI
   - Unsupervised Learning

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