Artificial Neural Networks and Machine Learning - ICANN 2024 [electronic resource] : 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VII / edited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko.

-- Speech Processing. -- Breaking the Corpus Bottleneck for Multi-dialect Speech Recognition with Flexible Adapters. -- Developmental Predictive Coding Model for Early Infancy Mono- and Bilingual Vocal Continual Learning. -- T-DVAE: A Transformer-based Dynamical Variational Autoencoder for Speech. -- Natural Language Processing. -- A Generalizable Context-Aware Deep Learning Model for Abusive Language Detection. -- A Novel Graph Neural Network Based Model for Text Classification. -- ABSA Methodology Based on Interval-enhanced Talking-heads Attention Network. -- An Evaluation Dataset for Targeted Sentiment Analysis in Long-Form Chinese News Articles. -- Anti-Hate Speech Framework: Leveraging Hedging Hyperbolic Learning. -- Combining Data Generation and Active Learning for Low-Resource Question Answering. -- CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought. -- EKD: Effective Knowledge Distillation for Few-Shot Sentiment Analysis. -- End-to-End Training of Back-Translation Framework with Categorical Reparameterization Trick. -- Enhancing Zero-Shot Translation in Multilingual Neural Machine Translation: Focusing on obtaining Location-Agnostic Representations. -- Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding. -- Improve Shallow Decoder Based Transformer with Structured Expert Prediction. -- KELTP: Keyword-Enhanced Learned Token Pruning for Knowledge-Grounded Dialogue. -- Knowledge Base Question Generation via Data Augmentation with Dynamic-prompt. -- Lifelong Sentiment Classification Based on Adaptive Parameter Updating. -- Multi-stage vs Single-stage: A Local Information Focused Approach for Overlapping Event Extraction. -- PLIClass: Weakly Supervised Text Classification with Iterative Training and Denoisy Inference. -- Reinforced Keyphrase Genertion with Multi-Dimensional Reward. -- Reinforced Multi-Teacher Knowledge Distillation for Unsupervised Sentence Representation. -- Summarizing Like Human: Edit-Based Text Summarization with Keywords. -- Towards Persona-oriented LLM-generated Text Detection: Benchmark Dataset and Method. -- Use of Riemannian distance metric to verify topological similarity of acoustic and text domains. -- WKE: Word-level Knowledge Enrichment for Aspect Term Extraction. -- Language Modeling. -- A general-purpose material entity extraction method from large compound corpora using fine tuning of character features. -- Efficient Fine-tuning for Low-resource Tibetan Pre-trained Language Models. -- Enhancing LM's Task Adaptability: Powerful Post-Training Framework with Reinforcement Learning from Model Feedback. -- GL-NER: Generation-aware Large Language Models for Few-shot Named Entity Recognition.

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.

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