Artificial Neural Networks and Machine Learning - ICANN 2024 [electronic resource] : 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part IV / edited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko.

-- Brain-inspired ComputingBrain-inspired Computing. -- A Multiscale Resonant Spiking Neural Network for Music Classification. -- Masked Image Modeling as a Framework for Self-Supervised Learning across Eye Movements. -- Serial Order Codes for Dimensionality Reduction in the Learning of Higher-Order Rules and Compositionality in Planning. -- Sparsity aware Learning in Feedback-driven Differential Recurrent Neural Networks. -- Towards Scalable GPU-Accelerated SNN Training via Temporal Fusion. -- Cognitive and Computational Neuroscience. -- Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer. -- Biologically-plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-encoded Distributions. -- Dynamic Graph for Biological Memory Modeling: A System-Level Validation. -- EEG features learned by convolutional neural networks reflect alterations of social stimuli processing in autism. -- Estimate of the Storage Capacity of q-Correlated Patterns in Hopfield Neural Networks. -- An Accuracy-Shaping Mechanism for Competitive Distributed Learning. -- Explainable Artificial Intelligence. -- Counterfactual Contrastive Learning for Fine Grained Image Classification. -- Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space. -- Exploring Task-Specific Dimensions in Word Embeddings Through Automatic Rule Learning. -- Generally-Occurring Model Change for Robust Counterfactual Explanations. -- Model Based Clustering of Time Series Utilizing Expert ODEs. -- Towards Generalizable and Interpretable AI-Modified Image Detectors. -- Understanding Deep Networks via Multiscale Perturbations. -- Robotics. -- Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning. -- Neural Formation A*: A Knowledge-Data Hybrid-Driven Path Planning Algorithm for Multi-agent Formation Cooperation. -- Robust Navigation for Unmanned Surface Vehicle Utilizing Improved Distributional Soft Actor-Critic. -- When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration. -- Reinforcement Learning. -- Asymmetric Actor-Critic for Adapting to Changing Environments in Reinforcement Learning. -- Building surrogate models using trajectories of agents trained by Reinforcement Learning. -- Demand-Responsive Transport Dynamic Scheduling Optimization Based on Multi-Agent Reinforcement Learning under Mixed Demand. -- Dual Action Policy for Robust Sim-to-Real Reinforcement Learning. -- Enhancing Visual Generalization in Reinforcement Learning with Cycling Augmentation. -- Speeding up Meta-Exploration via Latent Representation.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17-20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

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