Advanced Intelligent Computing Technology and Applications

.- Neural Networks.
.- Domain Attention and Confidence-Aware Unsupervised Domain Adaptation Network.
.- Enhanced Spatio-Temporal Extended Pattern Diffusion Network for Traffic Flow Forecasting.
.- Research on Digital 3D Reconstruction of Cultural Relics Using Depth Diffusion Gaussian Splatting.
.- Improve Self-supervision Learning by Enhancing Invariant Information.
.- FQuant: Fast Quantization with Adaptive Resolution via the Clustering Algorithm.
.- Solving Seismic Wave Propagation Using an Adaptive Collocation Point[1]Based Physics-Informed Neural Network.
.- Improving Story Visualization via Attribute Encoding and Adaptive Attention.
.- Text Generation Image Model Based on Gated Convolution Attention Generation Adversarial Network.
.- EFF-ViT: A Vision Transformer with Feature Enhancement and Fusion for Fine-Grained Visual Classification.
.- Concept-based Reasoning Explanation for Deep Neural Networks: Drawing on Human Decision-making.
.- OOD Generalization of GNNs through Causal Stabilization Learning.
.- BiGuidedPrompt: Dynamic Bidirectional Guided Multimodal Prompt Learning.
.- Flight Arrival Delay Prediction Based on Bidirectional Temporal Convolutional Networks.
.- Multi-Granularity Filter Pruning for Deep Neural Network Compression.
.- EEGTCT: Electroencephalogram-based Chinese Text Decoding.
.- Decoupled Graph Neural Networks with Hybrid Data Augmentation.
.- Non-invasive Emotion Perception from Gait by Sparse and Spatial-Temporal Excitation based Graph Convolutional Network.
.- Resource-Constrained Scheduling in Containerized Edge Computing Using Graph Transformer-Enhanced DQN.
.- REAMP: A Redundancy Elimination System for AMP-GNN Acceleration.
.- AGD-Net: An Attention-Guided Network for Joint Background Suppression and Defect-Aware Detail Enhancement.
.- Guarding Graph Neural Networks Against Backdoor Attacks-A Training Loss Dynamics Approach.
.- Dual-Resolution Segmentation Network Utilizing Multi-Scale Features for Metal Defect Detection.
.- Robust Multiview Point Cloud Registration with Reliable Sparse Graph and Adaptive Reweighting.
.- Keyphrase Generation Based on the Fusion of Sequence and Word Graph Features.
.- Decoupled Dual-Path Diffusion: Precise Spatial-Semantic Modeling for Human-Object Interaction Generation.
.- Enhancing Virtual Try-On with Text-Image Fusion Guidance.
.- Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples.
.- A Wireless Collaborated Inference Acceleration Framework for Plant Disease Recognition.
.- Unsupervised Learning for Solving the Graph Edit Distance.
.- MCE:One-Shot method to relation extraction based on LLMs.
.- Device Anomaly Sound Detection Based on Unsupervised Adversarial Distillation Domain Adaptation.
.- Multi-Bit Mechanism: Towards Ultra-Low Time Steps for Spiking Neural Networks.
.- FB-SAM: An Effective Learning Framework for First Break Picking Based on the SAM Model with Limited Data.
.- Feature Visualization in 3D Convolutional Neural Networks.
.- Multi-Dimensional Spatiotemporal Modeling for Multimodal Emotion Recognition in Conversations.
.- Multi-Scale Periodic Residual State Space Model for Time Series Forecasting.
.- YOLO-MAOD: An Algorithm for Ground Object Detection in Open-Pit Mine Based on Remote Sensing Images.
.- SimMix: Enhancing Label Consistency in Graph Mixup for Improved Graph Classification.
.- GUIDE: Learnable Deep Contrastive Graph Clustering with Centrality Guidance.
.- LEFF-YOLO: A Lightweight Cherry Tomato Detection YOLOv8 Network with Enhanced Feature Fusion.
.- Hierarchical Text Graph Learning for Inductive Text Classification.
.- Co-GNN: A Co-Optimization Framework for Memory and Computation in Sampling-based GNN Training.
.- FGSMS: Fine-Grained SM Scheduling for Efficient Deep Learning Computing.
.- Spatiotemporal PM10 Concentration Forecasting via Residual Attention Fusion and Mixture-of-Experts Enhanced Graph Neural Network.

• Erscheinungsdatum: 24.07.2025
• Autor/Autorin: De-Shuang Huang
• Reihe: Lecture Notes in Computer Science
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 93.8 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 539 Seiten
• ISBN: 9789819500062
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.