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..
Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization
Authors: Hancheng Min, Enrique Mallada, Rene Vidal
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on the MNIST dataset illustrate our theoretical findings. |
| Researcher Affiliation | Academia | Hancheng Min University of Pennsylvania EMAIL Enrique Mallada Johns Hopkins University EMAIL René Vidal University of Pennsylvania EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Numerical experiments on the MNIST dataset illustrate our theoretical findings. |
| Dataset Splits | No | The paper mentions using the MNIST dataset for numerical experiments but does not provide specific details on training, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., programming languages, libraries, frameworks, or solvers) used for the experiments. |
| Experiment Setup | Yes | We build a two-layer Re LU network with h = 50 neurons and initialize all entries of the weights as [W]ij i.i.d. N 0, α2 , vj i.i.d. N 0, α2 , i [n], j [h] with α = 10 6. Then we run gradient descent on both W and v with step size η = 2 10 3. |