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..
Directional convergence and alignment in deep learning
Authors: Ziwei Ji, Matus Telgarsky
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we additionally provide empirical support not just close to the theory (e.g., the Alex Net), but also on non-homogeneous networks (e.g., the Dense Net).The experiments in Figures 1 and 2 are performed in as standard a way as possible to highlight that directional convergence is a reliable property; full details are in Appendix A. Briefly, Figure 1 uses synthetic data and vanilla gradient descent... Figure 2 uses standard cifar firstly with a modified homogeneous Alex Net and secondly with an unmodified Dense Net |
| Researcher Affiliation | Academia | Ziwei Ji Matus Telgarsky EMAIL University of Illinois, Urbana-Champaign |
| Pseudocode | No | The paper contains mathematical theorems and proofs but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Figure 2 uses standard cifar firstly with a modified homogeneous Alex Net and secondly with an unmodified Dense Net |
| Dataset Splits | No | The paper mentions using synthetic data and standard CIFAR, but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | All computations were performed on standard CPUs. This is a vague statement and does not provide specific hardware details such as CPU models, number of cores, or memory specifications. |
| Software Dependencies | No | Pytorch [Paszke et al., 2019] was used for implementation. This mentions a software name but does not provide a specific version number. No other software with version numbers is listed. |
| Experiment Setup | Yes | Figure 1 uses synthetic data and vanilla gradient descent (no momentum, no weight decay, etc.) on a 10,000 node wide 2-layer squared Re LU network.Figure 2 uses standard cifar firstly with a modified homogeneous Alex Net and secondly with an unmodified Dense Net; SGD was used on cifar due to training set size. |