REVIEW 4 cited by
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
abstract
Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation. Based on a recent idea of stabilization of such algorithms in the scalar output case, we here consider the vectorial extension built on VKOGA. We introduce the so called $\gamma$-restricted VKOGA, comment on analytical properties and present numerical evaluation on data from a clinically relevant application, the modelling of the human spine. The experiments show that the new stabilized algorithms result in improved accuracy and stability over the non-stabilized algorithms.
Forward citations
Cited by 4 Pith papers
-
A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
An LMC multi-output GP is approximated by a one-dimensional chain model on which exact Gaussian message passing delivers linear-time inference and native partial-observation handling.
-
Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS)
SLiCS learns group-structured non-negative dictionaries that disentangle dense image embeddings into concept components, improving concept-filtered retrieval and enabling image-to-prompt generation.
-
State-Dependent Observation Noise Reintroduces Epistemic Value in Linear-Gaussian Active Inference
State-dependent observation noise, evaluated at a control-dependent belief mean, makes a linear-Gaussian active-inference agent's posterior covariance and (for scalar observations) epistemic value action-dependent, an...
-
A mapping of the Min-Sum decoder to reduction operations, and its implementation using CUDA kernels
The Min-Sum LDPC decoder is restated as masked matrix reductions and implemented with CUDA kernels that take the parity matrix as a runtime argument, with throughput measured on a CCSDS quasi-cyclic code.
Discussion (0). Continue with ORCID to comment.