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Found 6 papers, 1 papers with code
Towards Precision Healthcare: Robust Fusion of Time Series and Image Data
Our motivation comes from the important areas of predicting mortality and phenotyping where using different modalities of data could significantly improve our ability to predict.
MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs
Hence, in this work, we introduce a new framework for interpreting multivariate time series data by extracting and clustering the input representative patterns that highly activate CNN neurons.
MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities
Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection.
Focusing Knowledge-based Graph Argument Mining via Topic Modeling
We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence.
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation
Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret.
Online Multi-Label Classification: A Label Compression Method
Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization.
