Artificial Neural Networks and Machine Learning - ICANN 2023
32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26-29, 2023, Proceedings, Part VI
(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 Further Exploration of Deep Multi-Agent Reinforcement Learning with Hybrid Action Space.- Air-to-Ground Active Object Tracking via Reinforcement Learning.- Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-Processing of Recurrent Neural Networks.- Group-Agent Reinforcement Learning.- Improving Generalization of Multi-agent Reinforcement Learning through Domain-Invariant Feature Extraction.- Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning.- LIIVSR: A Unidirectional Recurrent Video Super-Resolution Framework with Gaussian Detail Enhancement and Local Information Interaction Modules.- Masked Scale-Recurrent Network for Incomplete Blurred Image Restoration.- Multi-fusion Recurrent Network for Argument Pair Extraction.- Pacesetter Learning For Large Scale Cooperative Multi-Agent Reinforcement Learning.- Stable Learning Algorithm Using Reducibility for Recurrent Neural Networks.- t-ConvESN: Temporal Convolution-Readout for Random Recurrent Neural Networks.- Adaptive Reservoir Neural Gas: An Effective Clustering Algorithm for Addressing Concept Drift in Real-Time Data Streams.- An Intelligent Dynamic Selection System Based on Nearest Temporal Windows for Time Series Forecasting.- Generating Sparse Counterfactual Explanations For Multivariate Time Series.- Graph Neural Network-Based Representation Learning for Medical Time Series.- Knowledge Forcing: Fusing Knowledge-Driven Approaches with LSTM for Time Series Forecasting.- MAGNet: Muti-scale Attention and Evolutionary Graph Structure for Long Sequence Time-Series Forecasting.- MIPCE: Generating Multiple Patches Counterfactual-changing Explanations for Time Series Classification.- Multi-Timestep-Ahead Prediction with Mixture of Experts for Embodied Question Answering.- Rethink the Top-u Attention in Sparse Self-attention for Long Sequence Time-Series
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Forecasting.- Temporal Attention Signatures for Interpretable Time-Series Prediction.- Time-Series Prediction of Calcium Carbonate Concentration in Flue Gas Desulfurization Equipment by Optimized Echo State Network.- WAG-NAT: Window Attention and Generator Based Non-Autoregressive Transformer for Time Series Forecasting.- A Novel Encoder and Label Assignment for Instance Segmentation.- A Transformer-based Framework for Biomedical Information Retrieval Systems.- A Transformer-Based Method for UAV-View Geo-Localization.- Cross-graph Transformer Network for Temporal Sentence Grounding.- EGCN: A Node Classification Model based on Transformer and Spatial Feature Attention GCN for Dynamic Graph.- Enhance Representational Differentiation Step By Step: A Two-Stage Encoder-Decoder Network for Implicit Discourse Relation Classification.- GenTC: Generative Transformer via Contrastive Learning for Receipt Information Extraction.- Hierarchical Classification for Symmetrized VI Trajectory Based on Lightweight Swin Transformer.- Hierarchical Vision and Language Transformer for Efficient Visual Dialog.- ICDT: Maintaining Interaction Consistency for Deformable Transformer with Multi-scale Features in HOI Detection.- Imbalanced Conditional Conv-Transformer For Mathematical Expression Recognition.- Knowledge Graph Transformer for Sequential Recommendation.- LorenTzE: Temporal Knowledge Graph Embedding based on Lorentz Transformation.- MFT: Multi-scale Fusion Transformer for Infrared and Visible Image Fusion.- NeuralODE-based Latent Trajectories into AutoEncoder Architecture for Surrogate Modelling of Parametrized High-dimensional Dynamical Systems.- RRecT: Chinese Text Recognition with Radical-enhanced Recognition Transformer.- S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution.- Self-adapted Positional Encoding in the Transformer Encoder for Named Entity Recognition.- SHGAE: Social Hypergraph AutoEncoder for Friendship Inference.- Temporal Deformable Transformer For Action Localization.- Trans-Cycle: Unpaired Image-to-Image Translation Network by Transformer.
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Bibliographische Angaben
- 2023, 1st ed. 2023, XXXV, 591 Seiten, 182 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: 3031442229
- ISBN-13: 9783031442223
Sprache:
Englisch
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