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Found 7 papers, 5 papers with code
Online Time Series Anomaly Detection with State Space Gaussian Processes
We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes.
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie.
Path Imputation Strategies for Signature Models of Irregular Time Series
The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.
Set Functions for Time Series
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications.
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks.
Ranked #1 on
Graph Classification
on MUTAG
(Mean Accuracy metric)
Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.
