標題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc [打印本頁] 作者: invigorating 時間: 2025-3-21 17:16
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024影響因子(影響力)
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024影響因子(影響力)學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024網(wǎng)絡公開度
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024網(wǎng)絡公開度學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024被引頻次
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024被引頻次學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024年度引用
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024年度引用學科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋學科排名
作者: ornithology 時間: 2025-3-21 21:08
3D Lattice Deformation Prediction with?Hierarchical Graph Attention Networkses the deformation predictions to those of physical simulations, achieving high fidelity in modeling real-world phenomena. In contrast to existing GNN architectures built for physical simulation approximation, the CGNN learns realistic folding behavior and lateral movement of individual lattice node作者: Focus-Words 時間: 2025-3-22 03:32
Beyond Homophily: Attributed Graph Anomaly Detection via?Heterophily-Aware Contrastive Learning Netwusing an unsupervised?edge discriminator. Additionally, a dual-channel encoder is designed?to capture representative node representations from discriminated edges. Extensive experiments on four public benchmark datasets demonstrate that our method is competitive with the most advanced baseline.作者: 邪惡的你 時間: 2025-3-22 08:10
CauchyGCN: Preserving Local Smoothness in?Graph Convolutional Networks via?a?Cauchy-Based Message-Paus strategies, including graph filters, k-hop jumps, and bounded penalties?to tackle this issue, these methods often fall short of explicitly capturing and preserving the local smoothness over the original topology. In this paper, we present CauchyGCN, which enhances preserving local smoothness in a作者: Cantankerous 時間: 2025-3-22 11:36 作者: amygdala 時間: 2025-3-22 13:14 作者: 指耕作 時間: 2025-3-22 17:27
Edged Weisfeiler-Lehman Algorithmddresses one key drawback in many GNNs that do not utilize any edge features of graph data. We evaluated the performance of proposed models using 12 edge-featured benchmark graph datasets and compared them with some state-of-the-art baseline models. Experimental results indicate that our proposed EG作者: debouch 時間: 2025-3-22 22:10 作者: obsession 時間: 2025-3-23 01:44
Graph-Guided Multi-view Text Classification: Advanced Solutions for?Fast Inference to enhance node sequence information. It integrates features?from multiple views through diverse strategies for both word-level?and text-level fusion. Secondly, to expand the receptive field of nodes, we propose a Remote Feature Extraction Module (RFE) to bridge?the difficult interaction gap betwee作者: AWRY 時間: 2025-3-23 09:04
Invariant Graph Contrastive Learning for?Mitigating Neighborhood Bias in?Graph Neural Network Based ing the shared variant vectors. Our experiments on three real-world public datasets demonstrate that the IGCL framework significantly outperforms existing baselines, offering a promising solution to overcome the neighborhood bias in GNN-based recommender systems. The source code of our work is avail作者: Inflamed 時間: 2025-3-23 13:39
Key Substructure-Driven Backdoor Attacks on?Graph Neural Networkscker-chosen target class key substructures, modifying few critical edges and nodes. Our approach across real datasets spanning diverse domains highlights its efficiency. The proposed methodology establishes a pioneering direction for refining backdoor attack techniques on GNNs.作者: 無可爭辯 時間: 2025-3-23 15:22
Missing Data Imputation via?Neighbor Data Feature-Enriched Neural Ordinary Differential Equationsetwork is then employed to learn adjacent information of neighboring variables. The temporal information is captured by applying a gate recurrent unit module, thereby obtaining a spatiotemporal prior. The decoder introduces an ordinary differential equation module to generate a series of continuous 作者: 尊嚴 時間: 2025-3-23 19:05 作者: 間諜活動 時間: 2025-3-24 00:37
STGNA: Spatial-Temporal Graph Convolutional Networks with Node Level Attention for Shortwave Communiatial-temporal patterns of shortwave communications parameters, yielding to enhanced forecasting accuracy. Comprehensive experiments on a targeted dataset demonstrate that our approach significantly outperforms other baselines in forecasting accuracy.作者: innate 時間: 2025-3-24 05:37
Virtual Nodes based Heterogeneous Graph Convolutional Neural Network for Efficient Long-Range Informormation aggregation with only 4 layers. Additionally, we demonstrate that VN-HGCN can serve as a versatile framework that can be seamlessly applied to other HGNN models, showcasing its generalizability. Empirical evaluations validate the effectiveness of VN-HGCN, and extensive experiments conducted作者: febrile 時間: 2025-3-24 08:20 作者: enchant 時間: 2025-3-24 12:56
An Enhanced Prompt-Based LLM Reasoning Scheme via?Knowledge Graph-Integrated Collaboration of the reasoning results. Experimental results show that our scheme significantly progressed across multiple datasets, notably achieving an improvement of over 10% on the QALD10 dataset compared to both the best baseline and the fine-tuned state-of-the-art (SOTA) models.作者: ATP861 時間: 2025-3-24 17:42 作者: Affirm 時間: 2025-3-24 22:39 作者: HACK 時間: 2025-3-25 02:50 作者: CANON 時間: 2025-3-25 06:14
The Current Main Types of Capsule Endoscopy,using an unsupervised?edge discriminator. Additionally, a dual-channel encoder is designed?to capture representative node representations from discriminated edges. Extensive experiments on four public benchmark datasets demonstrate that our method is competitive with the most advanced baseline.作者: 展覽 時間: 2025-3-25 10:33 作者: BALK 時間: 2025-3-25 15:11
Brad J. Martinsen PhD,Jamie L. Lohr MDo?learn more about the graph structure during the encoding process. Moreover, we mask and reconstruct both the structure and attribution of the graph and employ a graph neural network as the decoder?to enrich learning representations with compressed information. Finally, experimental results on node作者: Malleable 時間: 2025-3-25 18:20
https://doi.org/10.1007/978-1-59259-835-9 evaluate the proposed model on various benchmark datasets and compared?our results with several baseline graph neural network methods. CTQW-GraphSAGE achieves comparable results to the classical models on most of the selected datasets on node classification tasks.作者: 訓誡 時間: 2025-3-25 23:02 作者: Conquest 時間: 2025-3-26 01:26
Mechanical Aspects of Cardiac Performanceach,?the classifying capability of the GNN (measured via F1-macro, AUC, Recall) is improved by boosting the representation power of?the calculated embeddings that maximize the similarity between legitimate users while minimizing that between fraudsters?and legitimate users. Numerical experiments on 作者: Fibrinogen 時間: 2025-3-26 07:31
Daniel C. Sigg,Ayala Hezi-Yamit to enhance node sequence information. It integrates features?from multiple views through diverse strategies for both word-level?and text-level fusion. Secondly, to expand the receptive field of nodes, we propose a Remote Feature Extraction Module (RFE) to bridge?the difficult interaction gap betwee作者: 獸皮 時間: 2025-3-26 09:41
Daniel C. Sigg,Ayala Hezi-Yamiting the shared variant vectors. Our experiments on three real-world public datasets demonstrate that the IGCL framework significantly outperforms existing baselines, offering a promising solution to overcome the neighborhood bias in GNN-based recommender systems. The source code of our work is avail作者: 一起平行 時間: 2025-3-26 13:42
Anthony J. Weinhaus,Kenneth P. Robertscker-chosen target class key substructures, modifying few critical edges and nodes. Our approach across real datasets spanning diverse domains highlights its efficiency. The proposed methodology establishes a pioneering direction for refining backdoor attack techniques on GNNs.作者: botany 時間: 2025-3-26 16:48
Alexander J. Hill,Paul A. Iaizzoetwork is then employed to learn adjacent information of neighboring variables. The temporal information is captured by applying a gate recurrent unit module, thereby obtaining a spatiotemporal prior. The decoder introduces an ordinary differential equation module to generate a series of continuous 作者: muscle-fibers 時間: 2025-3-26 22:31 作者: GIDDY 時間: 2025-3-27 01:34
Paul A. Iaizzo PhD,Kevin Fitzgerald MSatial-temporal patterns of shortwave communications parameters, yielding to enhanced forecasting accuracy. Comprehensive experiments on a targeted dataset demonstrate that our approach significantly outperforms other baselines in forecasting accuracy.作者: 四溢 時間: 2025-3-27 07:10 作者: BLANK 時間: 2025-3-27 10:58 作者: AND 時間: 2025-3-27 15:10 作者: RAFF 時間: 2025-3-27 20:44 作者: Atrium 時間: 2025-3-27 21:55 作者: 致詞 時間: 2025-3-28 02:34 作者: 正式演說 時間: 2025-3-28 07:43 作者: Narrative 時間: 2025-3-28 13:04
ComMGAE: Community Aware Masked Graph AutoEncoderer, GAEs tend to over-emphasize proximity information at the expense of structural information, leading to relatively poor performance on some downstream tasks?such as node classification. To address this issue, we propose a?novel GAE framework via community, named Community aware Masked?Graph AutoE作者: Inclement 時間: 2025-3-28 14:39 作者: 疏忽 時間: 2025-3-28 22:05
Edged Weisfeiler-Lehman Algorithmn the representation of a node is updated by aggregating representations from itself and neighbor nodes recursively. Similar to the propagation-aggregation methodology, the Weisfeiler-Lehman (1-WL) algorithm tests isomorphism through color refinement according to color representations of a node and 作者: ADORE 時間: 2025-3-28 23:07 作者: 顛簸地移動 時間: 2025-3-29 03:16
Graph-Guided Multi-view Text Classification: Advanced Solutions for?Fast Inference of parameters and high memory requirements, making it difficult to implement in some real-time scenarios or limited resources. Therefore, researchers attempt?to use lightweight Graph Neural Networks(GNN) with excellent feature expression as an alternative solution. However, current GNN-based method作者: Cultivate 時間: 2025-3-29 08:37
Invariant Graph Contrastive Learning for?Mitigating Neighborhood Bias in?Graph Neural Network Based tite graphs. Despite the success of existing GNN-based recommender systems, they generally suffer from the . problem, which breaks the homophily assumption in various real-world recommendation scenarios. The neighborhood bias stems from the mixed complex local patterns caused by the diverse user pre作者: IRS 時間: 2025-3-29 11:32
Key Substructure-Driven Backdoor Attacks on?Graph Neural Networksing backdoor attacks have two main limitations: Firstly, they lack flexibility and effectiveness in associating predefined substructures with predicted labels, limiting their ability to influence the classifier. Secondly, the injection locations of these substructures lack stealth, failing to exploi作者: 友好關(guān)系 時間: 2025-3-29 19:35 作者: concubine 時間: 2025-3-29 23:01
Multi-graph Fusion and?Virtual Node Enhanced Graph Neural Networkses. However, many GNN-based techniques assume complete and accurate graph relations. Unfortunately, this assumption often diverges from reality, as real-world scenarios frequently exhibit missing and erroneous edges within graphs. Consequently, GNNs that rely solely on the original graph structure i作者: 秘方藥 時間: 2025-3-30 01:14 作者: 媽媽不開心 時間: 2025-3-30 06:33 作者: Endoscope 時間: 2025-3-30 09:35 作者: 容易做 時間: 2025-3-30 12:28
An Enhanced Prompt-Based LLM Reasoning Scheme via?Knowledge Graph-Integrated Collaborationenges in practical applications, including issues with hallucinations, inadequate knowledge updating, and limited transparency in the reasoning process. To overcome these limitations, this study innovatively proposes a collaborative training-free reasoning scheme involving tight cooperation between 作者: 變化 時間: 2025-3-30 20:26
Thermal Properties of Carbon Nanotubeoriginal attributes from the feature masking graph and the original graph. By combining the generation and contrastive modules, we calculate an anomaly score for each node. Extensive experiments on five benchmark datasets show our model outperforms current state-of-the-art models.作者: Sputum 時間: 2025-3-30 22:50
Boosting Attributed Graph Anomaly Detection via?Negative Sample Awarenessoriginal attributes from the feature masking graph and the original graph. By combining the generation and contrastive modules, we calculate an anomaly score for each node. Extensive experiments on five benchmark datasets show our model outperforms current state-of-the-art models.作者: BRIEF 時間: 2025-3-31 03:57 作者: 戰(zhàn)役 時間: 2025-3-31 08:02
Conference proceedings 2024ne 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 machin作者: 女上癮 時間: 2025-3-31 12:43 作者: Metamorphosis 時間: 2025-3-31 14:32 作者: 大廳 時間: 2025-3-31 18:28 作者: deadlock 時間: 2025-3-31 23:04 作者: 記憶法 時間: 2025-4-1 04:11 作者: Diverticulitis 時間: 2025-4-1 08:42
https://doi.org/10.1007/978-1-59259-835-9 networked data. Simultaneously, quantum machine learning has emerged as a rapidly advancing?and promising field, leveraging quantum computing principles to enhance machine learning models. Benefiting from the advancements in?both GNNs and quantum machine learning, we propose a novel hybrid?model ca作者: 缺乏 時間: 2025-4-1 11:14
Michael K. Loushin MD,Paul A. Iaizzo PhDn the representation of a node is updated by aggregating representations from itself and neighbor nodes recursively. Similar to the propagation-aggregation methodology, the Weisfeiler-Lehman (1-WL) algorithm tests isomorphism through color refinement according to color representations of a node and
