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 [1].
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. |