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..
Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions
Authors: Tao Sun, Qingsong Wang, Dongsheng Li, Bao Wang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on training Res Net110 from (He et al., 2016) for CIFAR-100 (Krizhevsky et al., 2009) classification using SIGNSGD and SIGNSGD-SIM with various batch sizes. |
| Researcher Affiliation | Academia | 1College of Computer, National University of Defense Technology, Hunan, China. 2University of Utah. |
| Pseudocode | Yes | Algorithm 1 SIGNSGD with SImple Momentum (SIGNSGD-SIM) |
| Open Source Code | No | Our code is based on open-source libraries2. 2github.com/akamaster/pytorch_resnet_ cifar10, github.com/epfml/error-feedback-SGD - The text states their code is based on open-source libraries and provides links to those libraries, but does not explicitly state their own implementation code is open-source or provide a link to it. |
| Open Datasets | Yes | We train various Res Net models from (He et al., 2016) on CIFAR-10/CIFAR-100 (Krizhevsky et al., 2009) |
| Dataset Splits | No | Both datasets are split into a training set of 50,000 images and a test set of 10,000 images. The paper mentions training and test sets, and data distribution for clients, but does not explicitly describe a validation set or its split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments were provided. |
| Software Dependencies | No | Our code is based on open-source libraries2. 2github.com/akamaster/pytorch_resnet_ cifar10, github.com/epfml/error-feedback-SGD - The paper mentions libraries but does not provide specific version numbers for them. |
| Experiment Setup | Yes | The learning rate is decimated twice during this time, first at 100 epochs and again at 150 epochs. The initial learning rate for a batch size of 128 is 1 10 3. (...) The momentum parameter of SIGNSGD-SIM is set to 0.9, and the weight decay for both algorithms is set to 1 10 4. |