Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
Authors: Robert Peach, Matteo Vinao-Carl, Nir Grossman, Michael David, Emma Mallas, David J. Sharp, Paresh A. Malhotra, Pierre Vandergheynst, Adam Gosztolai
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that RVGP possesses global regularity over the manifold, which allows it to super-resolve and inpaint vector fields while preserving singularities. Furthermore, we use RVGP to reconstruct high-density neural dynamics derived from low-density EEG recordings in healthy individuals and Alzheimer s patients. We show that vector field singularities are important disease markers and that their reconstruction leads to a classification accuracy of disease states comparable to high-density recordings. |
| Researcher Affiliation | Academia | Robert L. Peach* University Hospital W urzburg peach r@ukw.de Matteo Vinao-Carl*, Nir Grossman, Michael David Imperial College London (* Indicates equal contribution) Emma Mallas, David Sharp, Paresh A. Malhotra Imperial College London Pierre Vandergheynst, Adam Gosztolai EPFL adam.gosztolai@epfl.ch |
| Pseudocode | No | The paper describes the proposed method mathematically and textually but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, "...our method is readily compatible with inducing point methods, which we provide an implementation of." (Section 4.2). However, it does not provide a specific link or explicit statement about the code being publicly released. |
| Open Datasets | No | The paper mentions collecting EEG data from Hammersmith Hospital in London and the UKDRI Care Research & Technology Centre (Section 5.2, Appendix A.1, A.7). However, it does not provide concrete access information (link, DOI, repository, or citation to a public dataset) for this data. |
| Dataset Splits | Yes | For binary classification, we used a linear support vector machine and computed the accuracy and ROC curves for each approach separately using 10-fold cross-validation. |
| Hardware Specification | No | The paper does not mention any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions "MATLAB R2023a" and "eeglab (Delorme & Makeig, 2004)". While MATLAB has a version, EEGLAB is a toolbox cited by its publication, not given a specific software version. No other specific software dependencies with versions are listed. |
| Experiment Setup | No | The paper describes the general setup of experiments, such as using K-nearest neighbour graph (K=5) for kernel construction, but it does not specify concrete hyperparameters like learning rates, batch sizes, number of epochs, or optimizer settings for RVGP training. |