TY  - BOOK
AU  - Wand,Michael
AU  - Malinovská,Kristína
AU  - Schmidhuber,Jürgen
AU  - Tetko,Igor V.
ED  - SpringerLink (Online service)
TI  - Artificial Neural Networks and Machine Learning - ICANN 2024: 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VI 
T2  - Lecture Notes in Computer Science,
SN  - 9783031723476
AV  - Q334-342 
U1  - 006.3 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Artificial intelligence
KW  - Computers
KW  - Application software
KW  - Computer networks 
KW  - Artificial Intelligence
KW  - Computing Milieux
KW  - Computer and Information Systems Applications
KW  - Computer Communication Networks
N1  -  -- 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-72347-6
ER  -