Artificial Neural Networks and Machine Learning - ICANN 2024

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 1720, 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.

Inhalt

.- 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.