Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL 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 EMAIL |
| 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. |