Artificial Neural Networks and Machine Learning - ICANN 2024

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 1720, 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.

Inhalt

.- 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.