Contextual Gaussian process bandits with neural networks
Contextual decision-making problems have witnessed extensive applications in various
fields such as online content recommendation, personalized healthcare, and autonomous …
fields such as online content recommendation, personalized healthcare, and autonomous …
A new distributional treatment for time series anomaly detection
Time series is traditionally treated with two main approaches, ie, the time domain approach
and the frequency domain approach. These approaches must rely on a sliding window so …
and the frequency domain approach. These approaches must rely on a sliding window so …
Netki: a kirchhoff index based statistical graph embedding in nearly linear time
Recent advancements in learning from graph-structured data have shown promising results
on the graph classification task. However, due to their high time complexities, making them …
on the graph classification task. However, due to their high time complexities, making them …
Partial ordered Wasserstein distance for sequential data
Measuring the distance between data sequences is a challenging problem, especially in the
presence of outliers and local distortions. Existing measures typically align the two …
presence of outliers and local distortions. Existing measures typically align the two …
SVM-based subspace optimization domain transfer method for unsupervised cross-domain time series classification
F Ma, C Wang, Z Zeng - Knowledge and Information Systems, 2023 - Springer
Time series classification on edge devices has received considerable attention in recent
years, and it is often conducted on the assumption that the training and testing data are …
years, and it is often conducted on the assumption that the training and testing data are …
Local Subsequence-Based Distribution for Time Series Clustering
Analyzing the properties of subsequences within time series can reveal hidden patterns and
improve the quality of time series clustering. However, most existing methods for …
improve the quality of time series clustering. However, most existing methods for …
Representation learning and forecasting for inter-related time series
J Zuo - 2022 - theses.hal.science
Time series is a common data type that has been applied to enormous real-life applications,
such as financial analysis, medical diagnosis, environmental monitoring, astronomical …
such as financial analysis, medical diagnosis, environmental monitoring, astronomical …
Machine Learning on Clinical Time Series: Classification and Representation Learning
M Moor - 2022 - research-collection.ethz.ch
The life sciences of the digital era are driven by its most fundamental and irreplaceable
currency: data. The advent of big data and machine learning (ML) algorithms has promised …
currency: data. The advent of big data and machine learning (ML) algorithms has promised …
Motifs and Manifolds Statistical and Topological Machine Learning for Characterising and Classifying Biomedical Time Series
C Bock - 2021 - research-collection.ethz.ch
The increased focus on evidence-based practice in the health sciences led to a plethora of
(un) organised and digitised data. In conjunction with the availability of technological …
(un) organised and digitised data. In conjunction with the availability of technological …
[PDF][PDF] Apprentissage de représentations et prédiction pour des séries-temporelles inter-dépendantes
ZUO **gwei - theses.hal.science
Les séries temporelles sont un type de données endémique dans de nombreux domaines
d'applications, telles que l'analyse financière, la surveillance de l'environnement ou encore …
d'applications, telles que l'analyse financière, la surveillance de l'environnement ou encore …