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..
Gradient Sparsification for Communication-Efficient Distributed Optimization
Authors: Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we conduct experiments to validate the effectiveness and efficiency of the proposed sparsification technique. |
| Researcher Affiliation | Collaboration | Jianqiao Wangni University of Pennsylvania Tencent AI Lab EMAIL Jialei Wang Two Sigma Investments EMAIL Ji Liu University of Rochester Tencent AI Lab EMAIL Tong Zhang Tencent AI Lab EMAIL |
| Pseudocode | Yes | Algorithm 1 A synchronous distributed optimization algorithm, Algorithm 2 Closed-form solution, Algorithm 3 Greedy algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. The provided link (https://arxiv.org/abs/1710.09854) is to a full version of the paper, not a code repository. |
| Open Datasets | Yes | We consider the convolutional neural networks (CNN) on the CIFAR-10 dataset with different settings. |
| Dataset Splits | No | The paper mentions using CIFAR-10, which has standard splits, and synthetic data, but does not explicitly provide specific percentages, sample counts, or citations to predefined splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments, only mentioning a 'shared memory multi-thread' setup. |
| Software Dependencies | No | The paper mentions specific optimization algorithms like ADAM and SGD, but does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | The mini-batch size is set to be 8 by default unless otherwise specified. and The step sizes are fine-tuned on each case... and The initial step size is set to 0.02. and The number of workers is set to 16 or 32, the regularization parameter is set to {0.5, 0.1, 0.05}, and the learning rate is chosen from {0.5, 0.25, 0.05, 0.25}. |