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..
BN-invariant Sharpness Regularizes the Training Model to Better Generalization
Authors: Mingyang Yi, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm achieves considerably better performance than vanilla SGD over various experiment settings. [...] We test our algorithm on CIFAR dataset [Krizhevsky et al., 2012], it has preferable results under large batch size compared with baselines (SGD, Entropy SGD). |
| Researcher Affiliation | Collaboration | Mingyang Yi1,2 , Huishuai Zhang3 , Wei Chen3 , Zhi-Ming Ma1,2 and Tie-Yan Liu3 1University of Chinese Academy of Sciences 2Academy of Mathematics and Systems Science 3 Microsoft Research EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 SGD with BN-Sharpness regularization |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | First we test the algorithm with fully batch normalized Le Net [Le Cun et al., 1998] to test the performance for CIFAR10 [Krizhevsky et al., 2012]. |
| Dataset Splits | Yes | First we test the algorithm with fully batch normalized Le Net [Le Cun et al., 1998] to test the performance for CIFAR10 [Krizhevsky et al., 2012]. [...] For SGDS, the δ in CIFAR10 is 5e-4 and in CIFAR100 is 1e-3, learning rate is 0.2 and decay by a factor 0.1 respectively in epoch 60, 120, 160. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Update rule is SGD with momentum by setting learning rate as 0.2 and decay it by a factor 0.1 respectively in epoch 60, 120, 160 and momentum parameter as 0.9. We use 10000 batch size, and 5e-4 weight decay ratio for all the three experiments. [...] For the experiments with regularized BN-Sharpness, we choose λ as 1e-4 which increase by a factor of 1.02 for each epoch. We set δ = 0.001, and the p is chosen as 2. |