Artificial Neural Networks and Machine Learning – ICANN 2025

34th International Conference on Artificial Neural Networks, Kaunas, Lithuania, September 9–12, 2025, Proceedings, Part I

AvWalter Senn,Marcello Sanguineti

Häftad, Engelska, 2025

Del 16068 i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2025-09-12
Mått:155 x 235 x 40 mm
Vikt:1 095 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:693
Förlag:Springer Nature Switzerland AG
ISBN:9783032045577

Utforska kategorier

Informationsteknik: allmänt inom Data och IT
Tillämpad datateknik inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

.- 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 Formal Language Recognition with Autoregressive Models..- Brain Generative Replay for Continual Learning..- Dynamic Ensembles Towards Out-Of-Distribution Generalization of Affect Models..- D2R: Dual Regularization Loss with Collaborative Adversarial Generation for Model Robustness..- The Power of Max Pooling Layer..- Firing rates and representational error in efficient spiking networks are bounded by design..- CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization..- Cascade Pre-Attention: Regulating Neuronal Activation Distributions in MetaFormer-Based Spiking Neural Networks..- MTL-SIMNAS: Task Similarity-Driven Neural Architecture Search for Enhanced Multi-Task Learning..- Towards Better Graph Anomaly Detection: A Performance-Aware Neural Architecture Search Approach..- Improving Stability of Parameter Sharing in Cooperative Multi-Agent Reinforcement Learning..- The Explainability-Performance Coefficient: A New Metric for Model Transparency..- GLFMamba-U: Global-Local Fused Mamba-Unet..- Continuous Fair SMOTE - Fairness-Aware Stream Learning from Imbalanced Data..- Evaluating the Impact of Data Curation on Off-Policy Reinforcement Learning..- Enhancing Graph Neural Networks with Mixup-Based Knowledge Distillation..- A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers..- FedP2PAvg: A Peer-to-Peer Collaborative Framework for Federated Learning in Non-IID Scenarios..- Correcting the Modified Stochastic Synaptic Model of Synaptic Dynamics - Refinement of Vesicle and Neurotransmitters Functions..- Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient..- Efficient ReliefF: A low-power optimization of ReliefF for resource-constrained devices..- Enhancing Adversarial Robustness through Multi-Objective Representation Learning..- Trustworthy Learning with Noisy Labels..- Effect of Neuromodulation on the Brain Dynamical Repertoire..- Classification of large data sets by neural networks: A probabilistic viewpoint..- Identification and Realization of a Class of Discrete Event Systems by Neural Networks -Timed Petri Nets..- Dopamine-modulated Learning and Decision-making with Neuromorphic Computing..- A Unified Platform to Evaluate STDP Learning Rule and Synapse Model using Pattern Recognition in a Spiking Neural Network..- XOOD: A Self-Supervised Algorithm for Detecting Out-of-Distribution Data for Image Classification..- Perpetual Generation: Online Learning of Linear State-Space Models from a Single Stream..- Accelerating Spatiotemporal Learning with minConvRNNs..- Full Integer Arithmetic Online Training for Spiking Neural Networks..- Regularised Loss Function for Goal Recognition as a Deep Learning Task..- Improving Consistency Distillation with Rectified Trajectories..- Merging versus Separating Replay Samples in Continual Learning..- Signal-to-noise difference as a correlate of class learning in neural networks..- Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay..- Supervised feature selection with class self-representation..- Complexity and Criticality in Neuro-Inspired Reservoirs..- A Fokker-Planck Perspective on the Flow of Information in Continuous Memory Neural Networks.