UID:
almahu_9949882737502882
Umfang:
XXXIII, 330 p. 104 illus., 95 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2024.
ISBN:
9783031723476
Serie:
Lecture Notes in Computer Science, 15021
Inhalt:
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17-20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.
Anmerkung:
-- Multimodality. -- ARIF: An Adaptive Attention-Based Cross-Modal Representation Integration Framework. -- BVRCC: Bootstrapping Video Retrieval via Cross-matching Correction. -- CAW: Confidence-based Adaptive Weighted Model for Multi-modal Entity Linking. -- Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieva. -- Exploring Interpretable Semantic Alignment for Multimodal Machine Translation. -- Modal fusion-Enhanced two-stream hashing network for Cross modal Retrieval. -- Text Visual Question Answering Based on Interactive Learning and Relationship Modeling. -- Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment. -- Federated Learning. -- Addressing the Privacy and Complexity of Urban Traffic Flow Prediction with Federated Learning and Spatiotemporal Graph Convolutional Networks. -- An Accuracy-Shaping Mechanism for Competitive Distributed Learning. -- Federated Adversarial Learning for Robust Autonomous Landing Runway Detection. -- FedInc: One-shot Federated Tuning for Collaborative Incident Recognition. -- Layer-wised Sparsification Based on Hypernetwork for Distributed NN Training. -- Security Assessment of Hierarchical Federated Deep Learning. -- Time Series Processing. -- ESSformer: Transformers with ESS Attention for Long-Term Series Forecasting. -- Fusion of image representations for time series classification with deep learning. -- HierNBeats: Hierarchical Neural Basis Expansion Analysis for Hierarchical Time Series Forecasting. -- Learning Seasonal-Trend Representations and Conditional Heteroskedasticity for Time Series Analysis. -- One Process Spatiotemporal Learning of Transformers via Vcls Token for Multivariate Time Series Forecasting. -- STformer: Spatio-Temporal Transformer for Multivariate Time Series Anomaly Detection. -- TF-CL:Time Series Forcasting Based on Time-Frequency Domain Contrastive Learning.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031723469
Weitere Ausg.:
Printed edition: ISBN 9783031723483
Sprache:
Englisch
DOI:
10.1007/978-3-031-72347-6
URL:
https://doi.org/10.1007/978-3-031-72347-6
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