Laplace Approximation For Tensor Train Kernel Machines In System Identification
PositiveArtificial Intelligence
- A new study introduces a Bayesian tensor train kernel machine that utilizes Laplace approximation to enhance Gaussian process regression in system identification. This method addresses scalability issues by estimating the posterior distribution over a selected tensor train core while employing variational inference for hyperparameter precision. Experiments indicate that core selection is largely independent of tensor train ranks and feature structures, achieving significantly faster training times.
- The development of this Bayesian tensor train kernel machine is significant as it offers a robust solution to the computational challenges faced in Gaussian process regression, particularly in high-dimensional data scenarios. By replacing traditional cross-validation with variational inference, the method not only improves efficiency but also maintains accuracy, which is crucial for applications in dynamic systems and machine learning.
- This advancement reflects a broader trend in artificial intelligence where researchers are increasingly focusing on improving the scalability and efficiency of probabilistic models. The integration of variational inference and tensor networks is becoming a common theme, as seen in various studies addressing complex Bayesian models and optimization techniques. These developments highlight the ongoing efforts to refine machine learning methodologies, particularly in handling large datasets and enhancing model interpretability.
— via World Pulse Now AI Editorial System
