• Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
Band 15022 - 11%

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

11% sparen

65,99 € UVP 74,89 €

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.09.2024

Abbildungen

XXXIII, 450 p. 102 illus., 91 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

450

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

727 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72349-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.09.2024

Abbildungen

XXXIII, 450 p. 102 illus., 91 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

450

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

727 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72349-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)

  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • .- 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.