A Further Exploration of Deep Multi-Agent Reinforcement Learning with Hybrid Action Space -- Air-to-Ground Active Object Tracking via Reinforcement Learning -- Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-Processing of Recurrent Neural Networks -- Group-Agent Reinforcement Learning -- Improving Generalization of Multi-agent Reinforcement Learning through Domain-Invariant Feature Extraction -- Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning -- LIIVSR: A Unidirectional Recurrent Video Super-Resolution Framework with Gaussian Detail Enhancement and Local Information Interaction Modules -- Masked Scale-Recurrent Network for Incomplete Blurred Image Restoration -- Multi-fusion Recurrent Network for Argument Pair Extraction -- Pacesetter Learning For Large Scale Cooperative Multi-Agent Reinforcement Learning -- Stable Learning Algorithm Using Reducibility for Recurrent Neural Networks -- t-ConvESN: Temporal Convolution-Readout for Random Recurrent Neural Networks -- Adaptive Reservoir Neural Gas: An Effective Clustering Algorithm for Addressing Concept Drift in Real-Time Data Streams -- An Intelligent Dynamic Selection System Based on Nearest Temporal Windows for Time Series Forecasting -- Generating Sparse Counterfactual Explanations For Multivariate Time Series -- Graph Neural Network-Based Representation Learning for Medical Time Series -- Knowledge Forcing: Fusing Knowledge-Driven Approaches with LSTM for Time Series Forecasting -- MAGNet: Muti-scale Attention and Evolutionary Graph Structure for Long Sequence Time-Series Forecasting -- MIPCE: Generating Multiple Patches Counterfactual-changing Explanations for Time Series Classification -- Multi-Timestep-Ahead Prediction with Mixture of Experts for Embodied Question Answering -- Rethink the Top-u Attention in Sparse Self-attention for Long Sequence Time-Series Forecasting -- Temporal Attention Signatures for Interpretable Time-Series Prediction -- Time-Series Prediction of Calcium Carbonate Concentration in Flue Gas Desulfurization Equipment by Optimized Echo State Network -- WAG-NAT: Window Attention and Generator Based Non-Autoregressive Transformer for Time Series Forecasting -- A Novel Encoder and Label Assignment for Instance Segmentation -- A Transformer-based Framework for Biomedical Information Retrieval Systems -- A Transformer-Based Method for UAV-View Geo-Localization -- Cross-graph Transformer Network for Temporal Sentence Grounding -- EGCN: A Node Classification Model based on Transformer and Spatial Feature Attention GCN for Dynamic Graph -- Enhance Representational Differentiation Step By Step: A Two-Stage Encoder-Decoder Network for Implicit Discourse Relation Classification -- GenTC: Generative Transformer via Contrastive Learning for Receipt Information Extraction -- Hierarchical Classification for Symmetrized VI Trajectory Based on Lightweight Swin Transformer -- Hierarchical Vision and Language Transformer for Efficient Visual Dialog -- ICDT: Maintaining Interaction Consistency for Deformable Transformer with Multi-scale Features in HOI Detection -- Imbalanced Conditional Conv-Transformer For Mathematical Expression Recognition -- Knowledge Graph Transformer for Sequential Recommendation -- LorenTzE: Temporal Knowledge Graph Embedding based on Lorentz Transformation -- MFT: Multi-scale Fusion Transformer for Infrared and Visible Image Fusion -- NeuralODE-based Latent Trajectories into AutoEncoder Architecture for Surrogate Modelling of Parametrized High-dimensional Dynamical Systems -- RRecT: Chinese Text Recognition with Radical-enhanced Recognition Transformer -- S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution -- Self-adapted Positional Encoding in the Transformer Encoder for Named Entity Recognition -- SHGAE: Social Hypergraph AutoEncoder for Friendship Inference -- Temporal Deformable Transformer For Action Localization -- Trans-Cycle: Unpaired Image-to-Image Translation Network by Transformer.

The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26-29, 2023. The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications. .