Artificial neural networks and machine learning - ICANN 2024 Part 6
.- 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...
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| Weitere Verfasser: | , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
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| Schriftenreihe: | Lecture notes in computer science
15021 |
| Schlagworte: |
Angewandte Informatik
> COM094000
> COMPUTERS / Computer Science
> COMPUTERS / Computer Vision & Pattern Recognition
> COMPUTERS / Hardware / Network Hardware
> COMPUTERS / Neural Networks
> Computer vision
> Information technology: general issues
> Machine learning
> Maschinelles Lernen
> Maschinelles Sehen, Bildverstehen
> Network hardware
> Netzwerk-Hardware
> Neural networks & fuzzy systems
> Neuronale Netze und Fuzzysysteme
> Konferenzschrift
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| Zusammenfassung: | .- 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 SeriesAnalysis..- 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. 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 |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xxxiii, 330 Seiten Diagramme |
| ISBN: | 9783031723469 978-3-031-72346-9 |