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..
Sparsified SGD with Memory
Authors: Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments to illustrate the theoretical findings and the good scalability for distributed applications. |
| Researcher Affiliation | Academia | Machine Learning and Optimization Laboratory (MLO) EPFL, Switzerland |
| Pseudocode | Yes | The pseudocode is given in Algorithm 1. |
| Open Source Code | Yes | Our code is open-source and publicly available at github.com/epfml/sparsified SGD. |
| Open Datasets | Yes | Datasets. We consider a dense dataset, epsilon [35], as well as a sparse dataset, RCV1 [20] where we train on the larger test set. |
| Dataset Splits | No | The paper mentions using datasets for training but does not provide specific details on training/validation/test splits, such as percentages, sample counts, or explicit splitting methodology. |
| Hardware Specification | Yes | Experiments were run on an Ubuntu 18.04 machine with a 24 cores processor Intel Xeon CPU E5-2680 v3 @ 2.50GHz. |
| Software Dependencies | No | The paper mentions 'Python3', 'numpy library', and 'scikit-learn' but does not provide specific version numbers for the libraries. |
| Experiment Setup | Yes | We study the convergence of the method using the stepsizes ηt = γ/(λ(t+a)) and hyperparameters γ and a set as in Table 2. |