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..
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
Authors: Wei Hu, Lechao Xiao, Jeffrey Pennington
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide empirical evidence to support the results in Sections 4 and 5. To study how depth and width affect convergence speed of gradient descent under orthogonal and Gaussian initialization schemes, we train a family of linear networks with their widths ranging from 10 to 1000 and depths from 1 to 700, on a fixed synthetic dataset (X, Y ). |
| Researcher Affiliation | Collaboration | Wei Hu Princeton University EMAIL Lechao Xiao Google Brain EMAIL Jeffrey Pennington Google Brain EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | We choose X R1024 16 and W R10 1024, and set Y = W X. Entries in X and W are drawn i.i.d. from N(0, 1). |
| Dataset Splits | No | The paper mentions a 'fixed synthetic dataset' and 'training loss' but does not provide explicit details about training, validation, or test splits. |
| Hardware Specification | No | The paper does not specify any details about the hardware used for the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers. |
| Experiment Setup | Yes | To study how depth and width affect convergence speed of gradient descent under orthogonal and Gaussian initialization schemes, we train a family of linear networks with their widths ranging from 10 to 1000 and depths from 1 to 700, on a fixed synthetic dataset (X, Y ). Each network is trained using gradient descent staring from both Gaussian and orthogonal initializations. |