5 papers
Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification
Albert Saiapin, Kim Batselier
Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kerne…
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Albert Saiapin, Kim Batselier
Uncertainty estimation is essential for robust decision-making in the presence of ambiguous or out-of-distribution inputs. Gaussian Processes (GPs) are classical kernel-based model…
Tensor Network Based Feature Learning Model
Albert Saiapin, Kim Batselier
Many approximations were suggested to circumvent the cubic complexity of kernel-based algorithms, allowing their application to large-scale datasets. One strategy is to consider th…
Laplace Approximation For Tensor Train Kernel Machines In System Identification
Albert Saiapin, Kim Batselier
To address the scalability limitations of Gaussian process (GP) regression, several approximation techniques have been proposed. One such method is based on tensor networks, which…
Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
Albert Saiapin, Ivan Oseledets, Evgeny Frolov
In production applications of recommender systems, a continuous data flow is employed to update models in real-time. Many recommender models often require complete retraining to ad…