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Found 9 papers, 2 papers with code
Multivariate Time Series Forecasting with Transfer Entropy Graph
Multivariate time series (MTS) forecasting is an essential problem in many fields.
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.
CoSimGNN: Towards Large-scale Graph Similarity Computation
In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores.
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network.
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting
In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed.
MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting
To characterize complex relations among variables, a relation embedding module is designed in MTHetGNN, where each variable is regarded as a graph node, and each type of edge represents a specific static or dynamic relationship.
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node.
Geometry-aware Two-scale PIFu Representation for Human Reconstruction
Our key idea is to exploit the complementary properties of depth denoising and 3D reconstruction, for learning a two-scale PIFu representation to reconstruct high-frequency facial details and consistent bodies separately.
