Gutscheinbedingungen

**Gültig bis 10.06.2026 / Gültig für gebrauchte Bücher / Mindestbestellwert 20,00 € / Einzelne Artikel können ausgeschlossen sein / Online auf www.bücher.de.de / Nicht kombinierbar mit anderen Gutscheinen oder Preisaktionen / Nur einmal pro Einkauf einlösbar / Gutschein wird auf max. 500€ Bestellwert angerechnet / Keine Barauszahlung / Nicht gültig für Versandkosten und Services

  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2023
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2023
Band 14258 - 10%

Artificial Neural Networks and Machine Learning – ICANN 2023 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part V

10% sparen

85,99 € UVP 96,29 €

inkl. gesetzl. MwSt., Versandkostenfrei

Lieferung nach Hause

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.09.2023

Herausgeber

Lazaros Iliadis + weitere

Verlag

Springer

Seitenzahl

589

Maße (L/B/H)

23,5/15,5/3,4 cm

Gewicht

937 g

Auflage

1st ed. 2023

Sprache

Englisch

ISBN

978-3-031-44191-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.09.2023

Herausgeber

Verlag

Springer

Seitenzahl

589

Maße (L/B/H)

23,5/15,5/3,4 cm

Gewicht

937 g

Auflage

1st ed. 2023

Sprache

Englisch

ISBN

978-3-031-44191-2

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

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: Artificial Neural Networks and Machine Learning – ICANN 2023
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2023
  • A Multi-Task Instruction with Chain of Thought Prompting Generative Framework for Few-Shot Named Entity Recognition.- ANODE-GAN: Incomplete Time Series Imputation by Augmented Neural ODE-based Generative Adversarial Networks.- Boosting Adversarial Transferability through Intermediate Feature.- DaCon: Multi-Domain Text Classification Using Domain Adversarial Contrastive Learning.- Exploring the Role of Recursive Convolutional Layer in Generative Adversarial Networks.- GC-GAN: Photo Cartoonization using Guided Cartoon Generative Adversarial Network.- Generating Distinctive Facial Images from Natural Language Descriptions via Spatial Map Fusion.- Generative Event Extraction via Internal Knowledge-enhanced Prompt Learning.- Improved attention mechanism and adversarial training for respiratory infectious disease text named entity recognition.- Low-frequency Features Optimization for Transferability Enhancement in Radar Target Adversarial Attack.- Multi-Convolution and Adaptive-stride Based Transferable Adversarial Attacks.- Multi-Source Open-Set Image Classification based on Deep Adversarial Domain Adaptation.- SAL: Salient Adversarial Attack with LRP Refinement.- Towards background and foreground color robustness with adversarial right for the right reasons.- Towards Robustness of Large Language Models on Text-to-SQL Task: An Adversarial and Cross-Domain Investigation.- TransNoise: Transferable Universal Adversarial Noise for Adversarial Attack.- A spatial interpolation method based on meta-learning with spatial weighted neural networks.- Adapted Methods for GAN Vocoders via Skip-Connections ISTFT and Cooperative Structure.- An Efficient Approximation Method Based on Enhanced Physics-informed Neural Networks for Solving Localized Wave Solutions of PDEs.- Causal Interpretability and Uncertainty Estimation in Mixture Density Networks.- Connectionist Temporal Sequence Decoding: M-ary Hopfield Neural-network with Multi-limit cycle Formulation.- Explaining, Evaluating and Enhancing Neural Networks' Learned Representations.- Gated Variable Selection Neural Network for Multimodal Sleep Quality Assessment.- Generalized Thermostatistics and the Nonequilibrium Landscape Description of  Neural Network Dynamics.- Guiding the Comparison of Neural Network Local Robustness: An Empirical Study.- Information-Theoretically Secure Neural Network Training with Flexible Deployment.- LRP-GUS: A visual based data reduction algorithm for Neural Networks.- Mining and Injecting Legal Prior Knowledge to Improve the Generalization Ability of Neural Networks in Chinese Judgments.- Mixed-mode response of Nigral Dopaminergic neurons: an in silico study on SpiNNaker.- Pan-Sharpening with Global Multi-Scale Context Network.- Population Coding Can Greatly Improve Performance of Neural Networks: A Comparison.- Population CodingCan Greatly Improve Performance of Neural Networks: A Comparison.- QuasiNet: a neural network with trainable product layers.- Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings.- Real-time Adaptive Physical Sensor Processing with SNN Hardware.- Regularization for Hybrid N-Bit Weight Quantization of Neural Networks on Ultra-Low Power Microcontrollers.- SGNN: A new method for learning representations on signed networks.- SkaNet: Split Kernel Attention Network.- Syntax-Aware Complex-Valued Neural Machine Translation.- Traffic Flow Prediction Based on Multi-Type Characteristic Hybrid Graph Neural  Network.- Whisker Analysis Framework for Unrestricted Mice with Neural Networks.- Adaptive Segmentation Network for Scene Text Detection.- How to Extract and Interact? Nested Siamese Text Matching with Interaction and Extraction.- Label-guided Graphormer for Hierarchy Text Classification.- Text Semantic Matching Research Based on Parallel Dropout.- Towards Better Core Elements Extraction for Customer Service Dialogue Text.- UIT: Unifying Pre-Training Objectives for Image-Text Understanding.