A Multi-Task Instruction with Chain of Thought Prompting Generative Framework for Few-Shot Named Entity Recognition -- ANODE-GAN: Incomplete Time Series Imputation by Augmented Neural ODE-based Generative Adversarial Networks -- Boosting Adversarial Transferability through Intermediate Feature -- DaCon: Multi-Domain Text Classification Using Domain Adversarial Contrastive Learning -- Exploring the Role of Recursive Convolutional Layer in Generative Adversarial Networks -- GC-GAN: Photo Cartoonization using Guided Cartoon Generative Adversarial Network -- Generating Distinctive Facial Images from Natural Language Descriptions via Spatial Map Fusion -- Generative Event Extraction via Internal Knowledge-enhanced Prompt Learning -- Improved attention mechanism and adversarial training for respiratory infectious disease text named entity recognition -- Low-frequency Features Optimization for Transferability Enhancement in Radar Target Adversarial Attack -- Multi-Convolution and Adaptive-stride Based Transferable Adversarial Attacks -- Multi-Source Open-Set Image Classification based on Deep Adversarial Domain Adaptation -- SAL: Salient Adversarial Attack with LRP Refinement -- Towards background and foreground color robustness with adversarial right for the right reasons -- Towards Robustness of Large Language Models on Text-to-SQL Task: An Adversarial and Cross-Domain Investigation -- TransNoise: Transferable Universal Adversarial Noise for Adversarial Attack -- A spatial interpolation method based on meta-learning with spatial weighted neural networks -- Adapted Methods for GAN Vocoders via Skip-Connections ISTFT and Cooperative Structure -- An Efficient Approximation Method Based on Enhanced Physics-informed Neural Networks for Solving Localized Wave Solutions of PDEs -- Causal Interpretability and Uncertainty Estimation in Mixture Density Networks -- Connectionist Temporal Sequence Decoding: M-ary Hopfield Neural-network with Multi-limit cycle Formulation -- Explaining, Evaluating and Enhancing Neural Networks' Learned Representations -- Gated Variable Selection Neural Network for Multimodal Sleep Quality Assessment -- Generalized Thermostatistics and the Nonequilibrium Landscape Description of Neural Network Dynamics -- Guiding the Comparison of Neural Network Local Robustness: An Empirical Study -- Information-Theoretically Secure Neural Network Training with Flexible Deployment -- LRP-GUS: A visual based data reduction algorithm for Neural Networks -- Mining and Injecting Legal Prior Knowledge to Improve the Generalization Ability of Neural Networks in Chinese Judgments -- Mixed-mode response of Nigral Dopaminergic neurons: an in silico study on SpiNNaker -- Pan-Sharpening with Global Multi-Scale Context Network -- Population Coding Can Greatly Improve Performance of Neural Networks: A Comparison -- Population CodingCan Greatly Improve Performance of Neural Networks: A Comparison -- QuasiNet: a neural network with trainable product layers -- Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings -- Real-time Adaptive Physical Sensor Processing with SNN Hardware -- Regularization for Hybrid N-Bit Weight Quantization of Neural Networks on Ultra-Low Power Microcontrollers -- SGNN: A new method for learning representations on signed networks -- SkaNet: Split Kernel Attention Network -- Syntax-Aware Complex-Valued Neural Machine Translation -- Traffic Flow Prediction Based on Multi-Type Characteristic Hybrid Graph Neural Network -- Whisker Analysis Framework for Unrestricted Mice with Neural Networks -- Adaptive Segmentation Network for Scene Text Detection -- How to Extract and Interact? Nested Siamese Text Matching with Interaction and Extraction -- Label-guided Graphormer for Hierarchy Text Classification -- Text Semantic Matching Research Based on Parallel Dropout -- Towards Better Core Elements Extraction for Customer Service Dialogue Text -- UIT: Unifying Pre-Training Objectives for Image-Text Understanding.

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. .