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..
Detached Error Feedback for Distributed SGD with Random Sparsification
Authors: An Xu, Heng Huang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive deep learning experiments show significant empirical improvement of the proposed methods under various settings. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA. |
| Pseudocode | Yes | Algorithm 1 Detached Error Feedback (DEF(-A)). |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | Extensive deep image classification experiments on CIFAR-10/100 and Image Net show significant improvements of DEF(-A) over existing works with RBGS. |
| Dataset Splits | Yes | The model is trained for 200 epochs with a learning rate decay of 0.1 at epoch 100 and 150. Random cropping, random flipping, and standardization are applied as data augmentation techniques. |
| Hardware Specification | Yes | Each machine is equipped with 4 NVIDIA P40 GPUs and there are 16 workers (GPUs) in total. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'NCCL as the backend of the Py Torch distributed package' but does not specify version numbers for any software component. |
| Experiment Setup | Yes | The base learning rate is tuned from { , 0.1, 0.05, 0.01, } and the batch size is 128. The momentum constant is 0.9 and the weight decay is 5 10 4. The model is trained for 200 epochs with a learning rate decay of 0.1 at epoch 100 and 150. Random cropping, random flipping, and standardization are applied as data augmentation techniques. |