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..
Deep Networks and the Multiple Manifold Problem
Authors: Sam Buchanan, Dar Gilboa, John Wright
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is an analysis of the one-dimensional case of the multiple manifold problem, which reduces the analysis of the gradient descent dynamics to the construction of a certificate showing that a certain deterministic integral equation involving the network architecture and the structure of the data admits a solution of small norm. |
| Researcher Affiliation | Academia | Sam Buchanan Columbia University EMAIL Dar Gilboa Harvard University EMAIL John Wright Columbia University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (e.g., a repository link or explicit code release statement) for the methodology described in this paper. |
| Open Datasets | No | The paper describes a theoretical model problem where data is generated based on mathematical properties ('N i.i.d. samples from a distribution supported on the manifolds') rather than using a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on running experiments that would require specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental implementations requiring specific software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |