Artificial Neural Networks and Machine Learning ICANN 2025: 34th International [Paperback]

The four-volume set LNCS 16068-16071 constitutes the proceedings of the 34th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2025, held in Kaunas, Lithuania, September 9–12, 2025.
The 170 full papers and 8 abstracts included in these conference proceedings were carefully reviewed and selected from 375 submissions. The conference strongly values the synergy between theoretical progress and impactful real-world applications, and actively encourages contributions that demonstrate how artificial neural networks are being used to address pressing societal and technological challenges.

.- MRT-NAS: Boosting Training-free NAS via Manifold Regularization.
.- MSfusion: A Dynamic Model Splitting Approach for Resource Constrained Machines to Collaboratively Train Larger Models.
.- DeepCTL: Neural Branching-Time CTL Satisfiability Checking via Recursive Decision Trees.
.- MFMamba: A Hierarchical Weakly Causal Mamba with Multi-Scale Feature Fusion for Vision Tasks.
.- Characterizing trainability, expressivity and generalization of neural architecture with metrics from neural tangent kernel.
.- Unrolled Neural Adaptive Alternating Gradient Descent for NMF.
.- FedTP: Traceable Passport-based Ownership Verification for Federated Deep Neural Network Models.
.- Learning to Optimize Entropy in the Soft Actor-Critic.
.- Parallelizing Sharpness-Aware Minimization: A Semi-Asynchronous Small-Batch Approach.
.- Small transformer architectures for task switching.
.- Stochastic Covariance Regularization for Imbalanced Datasets.
.- Efficient Learning in Spiking Neural Networks - Introducing Feedback Alignment to the Reinforced Liquid State Machine.
.- Object-Centric Dreamer.
.- How Inductive Biases Affect OOD Generalization: An Investigation in&al3Z