Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning single-index models with shallow neural networks
Authors: Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 21,703 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,944 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our theoretical results with experiments in Section A. |
| Researcher Affiliation | Academia | Alberto Bietti New York University Joan Bruna New York University Clayton Sanford Columbia University Min Jae Song New York University |
| Pseudocode | Yes | The overall approach is described in Procedure 1. ... Procedure 1 Gradient Flow ... Procedure 2 Fine-Tuning |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Supplemental zip file. |
| Open Datasets | No | We focus on regression problems under a single-index model with Gaussian input data. Specifically, we assume d-dimensional inputs x γd := N(0, Id), and labels y = F (x) + = f (h , xi) + , where 2 Sd 1 and N(0, σ2) is an independent, additive Gaussian noise. |
| Dataset Splits | No | The paper discusses the theoretical sample size 'n' and uses synthetic data, but does not specify explicit training, validation, or test dataset splits needed for reproduction. |
| Hardware Specification | Yes | We ran all experiments on an Intel Core i9-9900K CPU @ 3.60GHz with 64GB RAM and a NVIDIA GeForce RTX 2080 Ti. |
| Software Dependencies | No | We generate synthetic data as specified in Section 3 and train our models using PyTorch. (No specific version numbers are provided for PyTorch or other software.) |
| Experiment Setup | Yes | The network has N = 1000 hidden units with biases drawn from N(0, 2) with = 1.2. We use the Adam optimizer with learning rate = 0.01, and default PyTorch parameters for momentum. We train for 100 epochs, with batch size 100. For the experiments in Figure 1, we set the dimension d = 50. For Figure 2, we set d = 100, N = 2000, batch size = 200. |