{"product_id":"advanced-intelligent-computing-technology-and-applications-ebook","title":"Advanced Intelligent Computing Technology and Applications","description":"\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e.- Neural Networks.\u003cbr\u003e\u003c\/strong\u003e.- Domain Attention and Confidence-Aware Unsupervised Domain Adaptation Network.\u003cbr\u003e.- Enhanced Spatio-Temporal Extended Pattern Diffusion Network for Traffic Flow Forecasting.\u003cbr\u003e.- Research on Digital 3D Reconstruction of Cultural Relics Using Depth Diffusion Gaussian Splatting.\u003cbr\u003e.- Improve Self-supervision Learning by Enhancing Invariant Information.\u003cbr\u003e.- FQuant: Fast Quantization with Adaptive Resolution via the Clustering Algorithm.\u003cbr\u003e.- Solving Seismic Wave Propagation Using an Adaptive Collocation Point\u003cspan style=\"mso-special-character: footnote;\"\u003e\u003c!-- [if !supportFootnotes]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;\"\u003e[1]\u003c\/span\u003e\u003c!--[endif]--\u003e\u003c\/span\u003eBased Physics-Informed Neural Network.\u003cbr\u003e.- Improving Story Visualization via Attribute Encoding and Adaptive Attention.\u003cbr\u003e.- Text Generation Image Model Based on Gated Convolution Attention Generation Adversarial Network.\u003cbr\u003e.- EFF-ViT: A Vision Transformer with Feature Enhancement and Fusion for Fine-Grained Visual Classification.\u003cbr\u003e.- Concept-based Reasoning Explanation for Deep Neural Networks: Drawing on Human Decision-making.\u003cbr\u003e.- OOD Generalization of GNNs through Causal Stabilization Learning.\u003cbr\u003e.- BiGuidedPrompt: Dynamic Bidirectional Guided Multimodal Prompt Learning.\u003cbr\u003e.- Flight Arrival Delay Prediction Based on Bidirectional Temporal Convolutional Networks.\u003cbr\u003e.- Multi-Granularity Filter Pruning for Deep Neural Network Compression.\u003cbr\u003e.- EEGTCT: Electroencephalogram-based Chinese Text Decoding.\u003cbr\u003e.- Decoupled Graph Neural Networks with Hybrid Data Augmentation.\u003cbr\u003e.- Non-invasive Emotion Perception from Gait by Sparse and Spatial-Temporal Excitation based Graph Convolutional Network.\u003cbr\u003e.- Resource-Constrained Scheduling in Containerized Edge Computing Using Graph Transformer-Enhanced DQN.\u003cbr\u003e.- REAMP: A Redundancy Elimination System for AMP-GNN Acceleration.\u003cbr\u003e.- AGD-Net: An Attention-Guided Network for Joint Background Suppression and Defect-Aware Detail Enhancement.\u003cbr\u003e.- Guarding Graph Neural Networks Against Backdoor Attacks-A Training Loss Dynamics Approach.\u003cbr\u003e.- Dual-Resolution Segmentation Network Utilizing Multi-Scale Features for Metal Defect Detection.\u003cbr\u003e.- Robust Multiview Point Cloud Registration with Reliable Sparse Graph and Adaptive Reweighting.\u003cbr\u003e.- Keyphrase Generation Based on the Fusion of Sequence and Word Graph Features.\u003cbr\u003e.- Decoupled Dual-Path Diffusion: Precise Spatial-Semantic Modeling for Human-Object Interaction Generation.\u003cbr\u003e.- Enhancing Virtual Try-On with Text-Image Fusion Guidance.\u003cbr\u003e.- Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples.\u003cbr\u003e.- A Wireless Collaborated Inference Acceleration Framework for Plant Disease Recognition.\u003cbr\u003e.- Unsupervised Learning for Solving the Graph Edit Distance.\u003cbr\u003e.- MCE:One-Shot method to relation extraction based on LLMs.\u003cbr\u003e.- Device Anomaly Sound Detection Based on Unsupervised Adversarial Distillation Domain Adaptation.\u003cbr\u003e.- Multi-Bit Mechanism: Towards Ultra-Low Time Steps for Spiking Neural Networks.\u003cbr\u003e.- FB-SAM: An Effective Learning Framework for First Break Picking Based on the SAM Model with Limited Data.\u003cbr\u003e.- Feature Visualization in 3D Convolutional Neural Networks.\u003cbr\u003e.- Multi-Dimensional Spatiotemporal Modeling for Multimodal Emotion Recognition in Conversations.\u003cbr\u003e.- Multi-Scale Periodic Residual State Space Model for Time Series Forecasting.\u003cbr\u003e.- YOLO-MAOD: An Algorithm for Ground Object Detection in Open-Pit Mine Based on Remote Sensing Images.\u003cbr\u003e.- SimMix: Enhancing Label Consistency in Graph Mixup for Improved Graph Classification.\u003cbr\u003e.- GUIDE: Learnable Deep Contrastive Graph Clustering with Centrality Guidance.\u003cbr\u003e.- LEFF-YOLO: A Lightweight Cherry Tomato Detection YOLOv8 Network with Enhanced Feature Fusion.\u003cbr\u003e.- Hierarchical Text Graph Learning for Inductive Text Classification.\u003cbr\u003e.- Co-GNN: A Co-Optimization Framework for Memory and Computation in Sampling-based GNN Training.\u003cbr\u003e.- FGSMS: Fine-Grained SM Scheduling for Efficient Deep Learning Computing.\u003cbr\u003e.- Spatiotemporal PM10 Concentration Forecasting via Residual Attention Fusion and Mixture-of-Experts Enhanced Graph Neural Network.\u003c\/p\u003e","brand":"De-Shuang Huang","offers":[{"title":"Default Title","offer_id":53653373256007,"sku":"9789819500062","price":96.29,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/files\/advanced-intelligent-computing-technology-and-appl-ebook-cover.webp?v=1775397933","url":"https:\/\/www.cinebuch.de\/products\/advanced-intelligent-computing-technology-and-applications-ebook","provider":"CineBuch","version":"1.0","type":"link"}