Search Results for author:
Found 12 papers, 8 papers with code
Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective
In this work, we first show that state-of-the-art embedding approaches that factorize a PPR-related matrix can be unified into a closed-form framework.
SmoothGNN: Smoothing-based GNN for Unsupervised Node Anomaly Detection
Through empirical and theoretical analysis of real-world node anomaly detection (NAD) datasets, we observe that anomalous and normal nodes show different patterns in the smoothing process, which can be leveraged to enhance NAD tasks.
KPG: Key Propagation Graph Generator for Rumor Detection based on Reinforcement Learning
In this paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement learning-based rumor detection framework that generates contextually coherent and informative propagation patterns for events with insufficient topology information, while also identifies indicative substructures for events with redundant and noisy propagation structures.
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness
Specifically, PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model, aiming to preserve the generalization features already captured by the pre-trained model.
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs.
Instant Graph Neural Networks for Dynamic Graphs
With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes.
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
Learning Based Proximity Matrix Factorization for Node Embedding
Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes.
Inductive Link Prediction for Nodes Having Only Attribute Information
In attributed graphs, both the structure and attribute information can be utilized for link prediction.
DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network
The average accuracy is 93. 56%, compared with 85. 36% from CFMask.
Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
Furthermore, we use our neural network model as a feature extractor for unsupervised clustering and find that unlabeled DES images can be grouped together in two distinct galaxy classes based on their morphology, which provides a heuristic check that the learning is successfully transferred to the classification of unlabelled DES images.
