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..
Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms
Authors: Ganesh Sundaramoorthi, Anthony Yezzi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now show empirical evidence to illustrate the behavior of our accelerated optimization by comparing it to gradient descent. |
| Researcher Affiliation | Collaboration | Ganesh Sundaramoorthi United Technologies Research Center East Hartford, CT 06118 EMAIL Anthony Yezzi School of Electrical & Computer Engineering Georgia Institute of Technology, Atlanta, GA 30332 EMAIL |
| Pseudocode | No | The paper describes the numerical discretization in text but does not include structured pseudocode or an algorithm block in the main body. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper mentions using "binary images" and "MR cardiac images" but does not provide specific access information (link, DOI, repository, or formal citation) for these datasets to be publicly available. |
| Dataset Splits | No | The paper describes image registration experiments which involve optimizing a cost functional, but it does not specify explicit training, validation, or test dataset splits in the conventional sense of supervised machine learning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers, such as programming languages, libraries, or solvers used for the implementation. |
| Experiment Setup | Yes | The initialization is φ(x) = ψ(x) = x, v(x) = 0, and ρ(x) = 1/|Ω| where |Ω| is the area the image. ... where α > 0 is a weight ... For gradient descent we choose t < 1/(4α); for accelerated gradient descent we have the additional evolution of the velocity (12), and our numerical scheme has CFL condition t < 1/(4α maxx Ω{|v(x)|, |Dv(x)|}). ... Here α = 5. |