Merkzettel Warenkorb
  • Produktbild: PRICAI 2024: Trends in Artificial Intelligence
  • Produktbild: PRICAI 2024: Trends in Artificial Intelligence
Band 15282

PRICAI 2024: Trends in Artificial Intelligence 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18–24, 2024, Proceedings, Part II

Aus der Reihe Lecture Notes in Computer Science

78,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Lieferung nach Hause

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.11.2024

Herausgeber

Rafik Hadfi + weitere

Verlag

Springer Singapore

Seitenzahl

465

Maße (L/B/H)

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

Gewicht

744 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9601-18-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.11.2024

Herausgeber

Verlag

Springer Singapore

Seitenzahl

465

Maße (L/B/H)

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

Gewicht

744 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9601-18-9

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: [email protected]

Kundinnen und Kunden meinen

0 Bewertungen

Informationen zu Bewertungen

Zur Abgabe einer Bewertung ist eine Anmeldung im Konto notwendig. Die Authentizität der Bewertungen wird von uns nicht überprüft. Wir behalten uns vor, Bewertungstexte, die unseren Richtlinien widersprechen, entsprechend zu kürzen oder zu löschen.

Die Bewertungen sind nach Format, Anzahl Sterne und Datum sortiert.

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kund*innen durch Ihre Meinung

Kundinnen und Kunden meinen

0 Bewertungen filtern
  • Produktbild: PRICAI 2024: Trends in Artificial Intelligence
  • Produktbild: PRICAI 2024: Trends in Artificial Intelligence

  • .- Deep Learning.

    .- STLB-GN: Spatio-Temporal Dual Graph Network with Learnable Bases.

    .- Rethinking the Reliability of Post-hoc Calibration Methods under Subpopulation Shift.

    .- Zero-shot Heterogeneous Graph Embedding via Semantic Extraction.

    .- TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data.

    .- Stock Market Index Movement Prediction using Partial Contextual Embedding BERT-LSTM.

    .- SCBC: A Supervised Single-cell Classification Method Based on Batch Correction for ATAC-seq Data.

    .- TS-CATMA: A Lung Cancer Electronic Nose Data Classification Method Based on Adversarial Training and Multi-Scale Attention.

    .- Visualizing the Unseen: Arabic Image-to-Story Generation Using Deep Learning Techniques.

    .- Federated Learning.

    .- Federated Prompt Tuning: When is it Necessary?.

    .- Dirichlet-Based Local Inconsistency Query Strategy for Active Domain Adaptation.

    .- FedSD: Cross-Heterogeneous Federated Learning Based on Self-Distillation.

    .- Personalized Federated Learning with Feature Alignment via Knowledge Distillation.

    .- Multi-Party Collaborative Hate Speech Study on Social Media via Personalized Federated Learning.

    .- Preserving Individual User’s Right to be Forgotten in Enterprise-Level Federated Learning.

    .- Generative AI.

    .- Dance Generation From Music with Enhanced Beat.

    .- Contrastive Prototype Network for Generative Zero-Shot learning.

    .- Steganography: An improved robust model for deep hidden network.

    .- Human- and AI-Generated Marketing Content Comparison Corpus, Evaluation, and Detection.

    .- Natural Language Processing.

    .- Mongolian-Chinese Cross-lingual Topic Detection Based on Knowledge Distillation and Contrastive Learning Methods.

    .- Emergence of Grounded Language Representations for Continuous Object Properties through Decentralized Embodied Learning.

    .- AI-facilitation for consensus-building by virtual discussion using large language models.

    .- False Positive Detection for Text-based Person Retrieval.

    .- An End-to-End Method for Chinese Spelling Error Detection and Correction.

    .- Dialogue Summarization based on Feature Extraction and Commonsense Injection.

    .- SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq .- Personalized Generation with Causal Inference.

    .- Document-Level Relation Extraction Model Based On Boundary Distance Loss And Long-Tail Relation Enhancement.

    .- MCQG: Reading Comprehension Multiple Choice Questions Generation based on Pre-trained Language Models.

    .- ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification.

    .- EC-PEFT: An Expertise-Centric Parameter-Efficient Fine-Tuning Framework for Large Language Models.

    .- Enhanced Classification of Delay Risk Sources in Road Construction Using Domain- Knowledge-Driven.

    .- Modeling the Structural and Semantic Features for Japanese Lyrics Generation of J-pop Songs.

    .- FINE-LMT: Fine-grained Feature Learning for Multi-Modal Machine Translation.

    .- Segmentation Strategies and Data Enrichment for Improved Abstractive Summarization of Burmese Language.

    .- Constrained Reasoning Chains for Enhancing Theory-of-Mind in Large Language Models.

    .- Spatial-Temporal Union Channel Enhancement for Continuous Sign Language Recognition.

    .- KLoB: a Benchmark for Assessing Knowledge Localization Methods in Language Models.

    .- Cross-lingual Entity Alignment Model based on Multi-entity Enhancement and Semantic Information.

    .- Large Language Models.

    .- A Decomposed-Distilled Sequential Framework for Text-to-Table Task with LLMs.

    .- Are Dense Retrieval Models Few-Shot Learners?.

    .- An Empirical Study of Leveraging PLMs and LLMs for Long-Text Summarization.

    .- A Novel MLLMs-based Two-stage Model for Zero-shot Multimodal Sentiment Analysis.

    .- DeepTTS: Enhanced Transformer-Based Text Spotter via Deep Interaction Between Detection and Recognition Tasks.