Call For Papers
Topics of Interest
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
How To Submit
Perspective Authors shall submit their paper in Springer LNCS/LNAI format. Instructions for Authors, LaTeX templates, etc. are available at the
Springer Lecture Notes on Artificial Intelligence (LNAI) web-site (see http://www.springer.com/it/computer-science/lncs/conference-proceedings-guidelines).
The maximum paper length is 12 pages. Submission of a paper constitutes a commitment that, if accepted, at least one of the Authors will complete an early registration to the workshop.
On-line submission via OpenReview through the following link: https://openreview.net/group?id=iapr.org/TC3/2026/Workshop/ANNPR&referrer=%5BHomepage%5D(%2F)#tab-your-consoles
For deadlines see the Important Dates Webpage
