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StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds
However, for many practical problems that work with time series data in the industry: (i) One usually does not have access to a robust physical model, (ii) The addition of noise can in of itself require large or difficult assumptions (for example, what probability distribution should be used?
The Volume of Non-Restricted Boltzmann Machines and Their Double Descent Model Complexity
The double descent risk phenomenon has received much interest in the machine learning and statistics community.
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective
The appearance of the double-descent risk phenomenon has received growing interest in the machine learning and statistics community, as it challenges well-understood notions behind the U-shaped train-test curves.
New Approaches to Inverse Structural Modification Theory using Random Projections
In many contexts the modal properties of a structure change, either due to the impact of a changing environment, fatigue, or due to the presence of structural damage.
A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic Systems
Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems.
