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 Distributions Generated by One-Layer ReLU Networks
Authors: Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are provided to support our analysis. We empirically evaluate our algorithm in terms of its dependence over the number of samples, dimension, and condition number (Figure 1). |
| Researcher Affiliation | Academia | Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi Department of Electrical and Computer Engineering University of Texas at Austin |
| Pseudocode | Yes | Algorithm 1: Learning a single-layer Re LU generative model. Algorithm 2: Norm Bias Est. Algorithm 3: Proj SGD. |
| Open Source Code | Yes | Code to reproduce our result8 can be found at https://github.com/wushanshan/ density Estimation. |
| Open Datasets | No | The paper mentions generating W and b as random matrices/vectors for experiments ('we generate W as a random orthonormal matrix; we generate b as a random normal vector'), implying synthetic data, but does not refer to a publicly available or open dataset. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test dataset splits. Experiments are conducted on generated samples, but no specific partitioning strategy for reproducibility is mentioned. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions hyper-parameters for the algorithms ('The hyper-parameters are B = 1 (in Algorithm 2), r = 3 and λ = 0.1 (in Algorithm 3)'), but does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | The hyper-parameters are B = 1 (in Algorithm 2), r = 3 and λ = 0.1 (in Algorithm 3). Fix d = 5 and Îș = 1. Middle: Fix n = 5 â 10^5 and Îș = 1. Right: Fix n = 5 â 10^5 and d = 5. Every point shows the mean and standard deviation across 10 runs. Each run corresponds to a different W and b. |