Theory and Algorithms -- A 3D UWB hybrid localization method based on BSR and L-AOA -- Unsupervised Feature Selection Using Both Similar and Dissimilar Structures -- STA-Net: Reconstruct Missing Temperature Data of Meteorological Stations Using a Spatiotemporal Attention Neural Network -- Embedding Entity and Relation for Knowledge Graph by Probability Directed Graph -- Solving the inverse problem of laser with complex-valued field by physics-informed neural networks -- Efficient Hierarchical Reinforcement Learning via Mutual Information Constrained Subgoal Discovery -- Accelerate Support Vector Clustering via Spectral Data Compression -- A Novel Iterative Fusion Multi-Task Learning Framework for Solving Dense Prediction -- Anti-Interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding -- CLF-AIAD: A Contrastive Learning Framework for Acoustic Industrial Anomaly Detection -- Prediction and analysis of acoustic displacement field using the method of neural network -- Graph Multi-Dimensional Feature Network -- CBDN: A Chinese short-text classification model based on Chinese BERT and fused deep neural networks -- Lead ASR Models to Generalize Better Using Approximated Bias-Variance Tradeof -- Human-guided Transfer Learning for Autonomous Robot -- Leveraging Two-scale Features to Enhance Fine-grained Object Retrieval -- Predefined-time Synchronization of Complex Networks with Disturbances by Using Sliding Mode Control -- Reward-Dependent and Locally Modulated Hebbian Rule for Pattern Classification -- Robust Iterative Hard Thresholding Algorithm for Fault Tolerant RBF Network -- Cross-lingual Knowledge Distillation via Flow-based Voice Conversion for Robust Polyglot Text-To-Speech -- A health evaluation algorithm for edge nodes based on LSTM -- A Comprehensive Review of Arabic Question Answering Datasets -- Solving Localized Wave Solutions of the Nonlinear PDEs using Physics-Constraint Deep Learning Method -- Graph Reinforcement Learning For Securing Critical Loads By E-mobility -- Human-Object Interaction Detection with Channel Aware Attention -- AAKD-Net:Attention-based Adversarial Knowledge Distillation Network for Image Classification -- A High-Performance Tensorial Evolutionary Computation for Solving Spatial Optimization Problems -- Towards better evaluations of class activation mapping and interpretability of CNNs -- Contrastive Learning-Based Music Recommendation Model -- A Memory Optimization Method for Distributed Training -- Unsupervised Monocular Depth Estimation with Semantic Reconstruction using Dual-Discriminator Generative Adversarial Networks -- Generating Spatiotemporal Trajectories with GANs and Conditional GANs -- Visual Navigation of Target-Driven Memory-Augmented Reinforcement Learning -- Recursive Constrained Maximum Versoria Criterion Algorithm for Adaptive Filtering -- Graph Pointer Network and Reinforcement Learning for Thinnest Path Problem -- Multi-Neuron Information Fusion for Direct Training Spiking Neural Networks -- Event-based Object Recognition Using Feature Fusion and Spiking Neural Networks -- Circular FC: Fast Fourier Transform Meets Fully Connected Layer For Convolutional Neural Network -- Accurate Latency Prediction of Deep Learning Model Inference under Dynamic Runtime Resource -- Robust LS-QSVM Implementation via Efficient Matrix Factorization and Eigenvalue Estimation -- An Adaptive Auxiliary Training Method of Autoencoders and its Application in Anomaly Detection -- Matrix Contrastive Learning for Short Text Clustering -- Sharpness-aware Minimization for Out-of-Distribution Generalization -- Rapid APT Detection in Resource-Constrained IoT Devices Using Global Vision Federated Learning (GV-FL).

The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.