Call For Papers: Main Track (special sessions included) and Position Papers Track

Main Track and Special Sessions

Deadline for submission is extended to 11th June 2026!!!

When submitting a paper to ANNPR, you can decide to submit for the main track, or for one of our special session (see the list here).

ANNPR 2026 invites papers that present original work in the areas of neural networks and machine learning oriented to pattern recognition,
focusing on their algorithmic, theoretical, and applied aspects. Topics of interest include, but are not limited to:

   Methodological Issues
     – Supervised, semi-supervised, unsupervised, and reinforcement learning
     – Deep learning and deep reinforcement learning
     – Feed-forward, recurrent, and convolutional neural networks
     – Hierarchical modular architectures and hybrid systems
     – Interpretability and explainability of neural networks
     – Generative models
     – Robustness & generalization of neural networks
     – Meta-learning, Auto-ML
     – Multiple classifier systems and ensemble methods
     – Kernel machines
     – Probabilistic graphical models

   Applications to Pattern Recognition
     – Image processing and segmentation
     – Object detection
     – NLP and conversational agents
     – Sensor-fusion and multi-modal processing
     – Biometrics, including sspeech and speaker recognition and segmentation
     – Data, text, and social media analytics
     – Bioinformatics/Cheminformatics and medical applications
     – Industrial applications, e.g. quality control and predictive maintenance
     – Data clustering

Position Papers Track

Deadline 4th of July 2026

The 12th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2026) invites submissions to its Position Papers Track.

Artificial intelligence, machine learning, and neural network research are advancing at an unprecedented pace. Alongside technical breakthroughs, the community faces important questions concerning future research directions, methodological challenges, evaluation standards, robustness, reproducibility, interpretability, sustainability, and the societal impact of AI systems.

The Position Papers Track provides a forum for researchers, practitioners, and industry experts to present well-argued perspectives, critical analyses, emerging visions, and provocative ideas that can inspire discussion and shape future research agendas in neural networks and pattern recognition.

Unlike regular research papers, position papers are not required to present extensive experimental results. Instead, submissions should articulate a clear standpoint, supported by sound arguments, evidence from the literature, or preliminary observations.

Topics of Interest

Topics include, but are not limited to:

• Future directions for neural networks and machine learning
• Foundation models and their role in pattern recognition
• Challenges in evaluation, benchmarking, and reproducibility
• Interpretability, explainability, and trustworthy AI
• Robustness, uncertainty estimation, and generalization
• Data-centric AI and dataset quality
• Human-centered AI and interactive learning systems
• Responsible, ethical, and sustainable AI
• Neural-symbolic and hybrid learning approaches
• Emerging applications of AI in science, healthcare, and industry
• Perspectives on multimodal learning and generative AI
• Open challenges and research roadmaps for the ANNPR community

Submissions: https://openreview.net/group?id=iapr.org/TC3/2026/Workshop/ANNPR_Position_Papers#tab-your-consoles