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..
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data
Authors: Dachao Lin, Ruoyu Sun, Zhihua Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also characterize our findings in experiments. In this section we conduct experiments to verify our theoretical analyses. |
| Researcher Affiliation | Academia | Dachao Lin1 Ruoyu Sun2 Zhihua Zhang3 1Academy for Advanced Interdisciplinary Studies, Peking University 2Department of Industrial and Enterprise Engineering, Coordinate Science Lab (affiliated) University of Illinois Urbana-Champaign 3School of Mathematical Sciences, Peking University |
| Pseudocode | No | The paper describes methods mathematically and in text, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | We construct simple dataset with x U(S1) and y(x) = sgn(v x) with v = (0, 1) . This describes a synthetic data generation process, not a publicly available dataset with concrete access details like a URL, DOI, or repository. |
| Dataset Splits | No | The paper uses a synthetically generated dataset and discusses training with SGD, but it does not specify explicit train/validation/test dataset splits or their percentages/counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'stochastic gradient descent (SGD)' but does not list any specific software libraries with version numbers (e.g., Python, PyTorch, TensorFlow versions) that would allow replication. |
| Experiment Setup | Yes | We use common stochastic gradient descent (SGD) with the batch size 1000 and the constant small learning rate 10 3. Moreover, we choose an initial value w(0) = we(0) = (0.6, 0.8) . In the deep linear network, we set WN(0) = u N, Wi(0) = ui+1u i with ui = 1, i = 2, . . . , N and u1 = we(0) to satisfy the balancedness conditions Eq. (13). |