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- Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis782
A novel hierarchical learning rate adaptive deep convolution neural network based on an improved algorithm that is well suited to the fault-diagnosis model and superior to other existing methods is proposed.
- Graph Neural Networks for Natural Language Processing: A Survey410
A new taxonomy of GNNs for NLP is proposed, which systematically organizes existing research of Gnns forNLP along three axes: graph construction, graph representation learning, and graph based encoder-decoder models.
- A Systematic Survey on Deep Generative Models for Graph Generation211
An extensive overview of the literature in the field of deep generative models for graph generation is provided and taxonomies of deep Generative Models for graphs for both unconditional and conditional graph generation are proposed respectively.
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- Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery85
An integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, trained in a greedy layer-wise fashion.
- Single Image Reflection Separation via Component Synergy78
This paper proposes a more general form of the superposition model by introducing a learnable residue term, which can effectively capture residual information during decomposition, guiding the separated layers to be complete.
- Compact Graph Structure Learning via Mutual Information Compression75
This paper theoretically prove that if the authors optimize basic views and final view based on mutual information, and keep their performance on labels simultaneously, the final view will be a minimal sufficient structure, named CoGSL.
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- A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery54
A hybrid technique based on convolutional neural network and support vector regression is proposed, which is used to promote feature extraction capability and multi-class classification in rolling element bearings and gears.
- Controllable Data Generation by Deep Learning: A Review52
This article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain, and highlights the promising future directions of controllable deep data generation.
- FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection46
FraudGT is introduced, a simple, effective, and efficient graph transformer (GT) model specifically designed for fraud detection in financial transaction graphs that achieves state-of-the-art performance in detecting fraudulent activities while demonstrating high throughput and significantly lower latency compared to existing methods.
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- Reversible Decoupling Network for Single Image Reflection Removal44
This work proposes a novel architecture called Reversible Decoupling Network (RDNet), which employs a reversible encoder to secure valuable information while flexibly decoupling transmission- and reflection-relevant features during the forward pass, and customize a transmission-rate-aware prompt generator to dynamically calibrate features, further boosting performance.
- Graph Neural Networks: Foundation, Frontiers and Applications43
This tutorial of Graph Neural Networks (GNNs): Foundation, Frontiers and Applications will cover a broad range of topics in graph neural networks, by reviewing and introducing the fundamental concepts and algorithms of GNNs, new research frontiers of Gnns, and broad and emerging applications with GNNS.
- Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics39
A two-pronged approach with malware detection at node-level, and confinement of malware at network-level is proposed, and to scale with ever-increasing network topology sizes, a novel multi-attribute graph translation that can predict the networkTopology and node state information when provided with a snapshot of topology and nodes-level malware infection is introduced.
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- Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation38
PolarBEV is proposed for vision-based uneven BEV representation learning, which keeps real-time inference speed on a single 2080Ti GPU, and outperforms other methods for both BEV semantic segmentation and BEV instance segmentation.
- Deep Graph Translation38
A novel graph-translation-generative-adversarial-nets (GT-GAN) model that transforms the source graphs into their target output graphs and significantly outperforms other baseline methods in terms of both effectiveness and scalability.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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