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..
Learning One-hidden-layer Neural Networks with Landscape Design
Authors: Rong Ge, Jason D. Lee, Tengyu Ma
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also prove finite sample complexity results and validate the results by simulations. |
| Researcher Affiliation | Collaboration | Rong Ge Computer Science Department Duke University EMAIL Jason D. Lee Data Sciences and Operations Department, University of Southern California EMAIL Tengyu Ma Facebook AI Research EMAIL |
| Pseudocode | No | The paper describes algorithms (e.g., Algorithm 1, Algorithm 2 in Section D), but these are presented as textual descriptions rather than structured pseudocode blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code for the methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions data being 'generated from a one-hidden-layer network' in simulations but does not refer to a publicly available dataset with concrete access information (link, DOI, formal citation). |
| Dataset Splits | No | The paper mentions 'fresh samples at each iteration' and 'test error' but does not specify any training/test/validation dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We used batch size 262144 in the experiment for G( ). However, in contrast, for the ˆσ2h2 + ˆσ4h4 we used batch size 8192 and for relu we used batch size 256. We decreased step-size by a factor of 4 every 5000 number of iterations after the error plateaus at 10000 iterations. |