Artificial Neural Networks and Machine Learning – ICANN 2024

33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VII

Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko (Herausgeber)

Buch | Softcover

XXXIII, 450 Seiten

2024 | 2024
Springer International Publishing (Verlag)
978-3-031-72349-0 (ISBN)

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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.

.- 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.

Erscheinungsdatum	17.09.2024
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XXXIII, 450 p. 102 illus., 91 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Artificial Intelligence • classification • Deep learning • generative models • graph neural networks • Image Processing • Large Language Models • machine learning • Neural networks • Reinforcement Learning • reservoir computing • Robotics • spiking neural networks
ISBN-10	3-031-72349-X / 303172349X
ISBN-13	978-3-031-72349-0 / 9783031723490
Zustand	Neuware