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..
Online Neural Connectivity Estimation with Noisy Group Testing
Authors: Anne Draelos, John Pearson
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested the performance of Algorithm 1 in both the offline (all data) and online (one test at a time) settings. |
| Researcher Affiliation | Academia | Anne Draelos Biostatistics & Bioinformatics Duke University EMAIL John M. Pearson Biostatistics & Bioinformatics Electrical & Computer Engineering Neurobiology Duke University EMAIL |
| Pseudocode | Yes | Algorithm 1 Dual decomposition inference |
| Open Source Code | Yes | Code can be found at github.com/pearsonlab/Binary Stim |
| Open Datasets | No | We used randomly generated binary graphs wij in which each link appeared independently with probability K/N. |
| Dataset Splits | No | The paper discusses 'offline (all data)' and 'online (one test at a time)' settings but does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | No | The paper mentions 'efficient GPU implementations' and 'our GPU implementation using Cu Py [35]' but does not provide specific hardware details like GPU model numbers or CPU specifications. |
| Software Dependencies | Yes | our GPU implementation using Cu Py [35] performed each gradient descent iteration in under 2 seconds. Adam [34] with step size 0.01, β1 = 0.9, and β2 = 0.999 for optimization in the offline setting. |
| Experiment Setup | Yes | Unless otherwise stated, we use a base case of N = 1000, K = N^0.3 ~8 incoming connections per neuron, S = 10 stimulated neurons per test, α = β = 0.05, µ = 0, σ = 0.1, and Adam [34] with step size 0.01, β1 = 0.9, and β2 = 0.999 for optimization in the offline setting, with convergence typically achieved within 50 steps. |