Learning Distributions Generated by One-Layer ReLU Networks
Authors: Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 κ = 1. Middle: Fix n = 5 ⋅ 10^5 and κ = 1. Right: Fix n = 5 ⋅ 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. |