Tensor network methods provide a structured approach to representing and manipulating high-dimensional data by decomposing global information into interconnected low-rank tensors. Originating in the ...

Tensor decomposition of high-dimensional data often struggles to capture semantically or physically meaningful structures, particularly when relying on reconstruction objectives and fixed-rank ...

Abstract: Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic ...

The master thesis of Tobias Engelhardt Rasmussen, to be performed in the fall of 2020 and handed in on the 12th of February 2021. Convolutional Neural Networks (CNNs) are widely used for image ...

Abstract: The low-rank tensor model has made great progress for hyperspectral image (HSI) restoration. Recently, the low-rank tensor methods have further been boosted with subspace learning by ...

One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the ...

There has been a growing interest in efficient numerical algorithms based on tensor networks and low-rank techniques to approximate high-dimensional functions and compute the numerical solution of ...

Another example of exponential savings thanks to tensor network methods, now in multidimensional integration (100's of thousands of variables) using a clever combination of Fourier techniques and the ...

📢 Article Alert! Our recent work on tensor-train (TT) methods for compressible flows is now published online in the Journal of Computational Physics! This is the first study to apply high-order ...