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Found 7 papers, 2 papers with code
Extracting Interpretable Local and Global Representations from Attention on Time Series
This paper targets two transformer attention based interpretability methods working with local abstraction and global representation, in the context of time series data.
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data
However, these methods have not been adapted for time series tasks to a great extent.
Local Exceptionality Detection in Time Series Using Subgroup Discovery
In this paper, we present a novel approach for local exceptionality detection on time series data.
A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring
In particular, when such complex data and their inherent relationships need to be formalized, complex network modeling and its resulting graph representations enable a wide range of powerful options.
Feature-Set-Engineering for Detecting Freezing of Gait in Parkinson's Disease using Deep Recurrent Neural Networks
Specifically, for detecting FoG episodes, we apply a deep RNN with Long Short-Term Memory cells.
Towards Generating Explanations for ASP-Based Link Analysis using Declarative Program Transformations
The explication and the generation of explanations are prominent topics in artificial intelligence and data science, in order to make methods and systems more transparent and understandable for humans.
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized.
