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
(Sprache: Englisch)
The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26-29, 2023.
The 426...
The 426...
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Klappentext zu „Artificial Neural Networks and Machine Learning - ICANN 2023 “
The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26-29, 2023.The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.
Inhaltsverzeichnis zu „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
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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.
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Bibliographische Angaben
- 2023, 1st ed. 2023, XXXV, 589 Seiten, 186 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Lazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne
- Verlag: Springer, Berlin
- ISBN-10: 3031441915
- ISBN-13: 9783031441912
Sprache:
Englisch
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