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.

Table of Contents:
.- 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.