A Comparative Study of Sentence Embedding Models for Assessing Semantic Variation -- A Deep Learning based Method for Generating Holographic Acoustic Fields from Phased Transducer Arrays -- A Depth-guided Attention Strategy for Crowd Counting -- A Noise Convolution Network for Tampering Detection -- Attention-based Feature Interaction Deep Factorization Machine for CTR Prediction -- Block-level Stiffness Analysis of Residual Networks -- CKNA: Kernel Hyperparameters Optimization Method for Group-wise CNNs -- Conditional Convolution Residual Network for Efficient Super-Resolution -- Cross Attention with Deep Local Features for Few-shot Image Classification -- Deep Video Compression Based on 3D Convolution Artifacts Removal and Attention Compression Module -- Deep-learning Based Three Channel Defocused Projection Profilometry -- Depthwise Convolution with Channel Mixer: Rethinking MLP in MetaFormer for Faster and More Accurate Vehicle Detection -- DLUIO: Detecting Useful Investor Opinions by Deep Learning -- Dynamic obstacle avoidance for unmanned aerial vehicle using dynamic vision sensor -- Empirical Study on the Effect of Residual Networks on the Expressiveness of Linear Regions -- Energy Complexity Model for Convolutional Neural Networks -- Enhancing the Interpretability of Deep Multi-Agent Reinforcement Learning via Neural Logic Reasoning -- Evidential Robust Deep Learning for Noisy Text2text Question Classification -- FBPFormer: Dynamic Convolutional Transformer for Global-Local-Contexual Facial Beauty Prediction -- Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks -- Interaction of Generalization and Out-of-Distribution Detection Capabilities in Deep Neural Networks -- Long-distance Pipeline Intrusion Warning Based on Environment Embedding From Distributed Optical Fiber Sensing -- LSA3D: Lightweight Separate Asynchronous 3D Convolutional Neural Network for Gait Recognition -- MADNet: EEG-based Depression Detection using a Deep Convolution Neural Network Framework with Multi-dimensional Attention -- Maintenance automation using deep learning methods a case study from the aerospace industry -- MCASleepNet: Multimodal channel attention-based deep neural network for automatic sleep staging -- Multi-label Image Deep Hashing with Hybrid Loss of Global Center and Local Alignment -- Multi-relation Representation Learning based Deep Network for Patent Classification -- One Hip Wonder: 1D-CNNs Reduce Sensor Requirements for Everyday Gait Analysis -- Patches Channel Attention For Human Sitting Posture Recognition -- RA-Net: A Deep Learning Approach based on Residual Structure and Attention Mechanism for Image Copy-move Forgery Detection -- Rethinking CNN Architectures in Transformer Detectors -- Robustness of Biologically-inspired filter-based ConvNet to Signal Perturbation -- Self-Supervised Graph Convolution for Video Moment Retrieval -- Siamese Network based on MLP and Multi-head Cross Attention for Visual Object Tracking -- Taper Residual Dense Network for Audio Super-Resolution -- VPNDroid: Malicious Android VPN detection using a CNN-RF method -- Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics -- BLR:A multi-modal sentiment analysis model -- Detecting Negative Sentiment on Sarcastic Tweets for Sentiment Analysis -- Local or Global: The Variation in the Encoding of Style Across Sentiment and Formality -- Prompt-oriented Fine-tuning Dual Bert for Aspect-Based Sentiment Analysis -- Towards Energy-Efficient Sentiment Classification with Spiking Neural Networks -- Using Masked Language Modeling to Enhance BERT-based Aspect-Based Sentiment Analysis for Affective Token Prediction.

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 and 9 short 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.