Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
An Improved Analysis of Stochastic Gradient Descent with Momentum
Authors: Yanli Liu, Yuan Gao, Wotao Yin
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 15,386 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,442 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments In this section, we verify our theoretical claims by numerical experiments. |
| Researcher Affiliation | Academia | Department of Mathematics, University of California, Los Angeles Department of IEOR, Columbia University |
| Pseudocode | Yes | Algorithm 1 Multistage SGDM Input: problem data f(x) as in (1), number of stages n, momentum weights {βi}n i=1 [0, 1), step sizes {αi}n i=1, and stage lengths {Ti}n i=1 at n stages, initialization x1 Rd and m0 = 0, iteration counter k = 1. 1: for i = 1, 2, ..., n do 2: α αi, β βi; 3: for j = 1, 2, ..., Ti do 4: Sample a minibatch ζk uniformly from the training data; 5: gk xl(xk, ζk); 6: mk βmk 1 + (1 β) gk; 7: xk+1 xk αmk; 8: k k + 1; 9: end for 10: end for 11: return x, which is generated by first choosing a stage l {1, 2, ...n} uniformly at random, and then choosing x {x T1+...+Tl 1+1, x T1+...+Tl 1+2, ..., x T1+...+Tl} uniformly at random; |
| Open Source Code | Yes | Our implementation is available at Git Hub1. 1https://github.com/gao-yuan-hangzhou/improved-analysis-sgdm |
| Open Datasets | Yes | The MNIST dataset consists of n = 60000 labeled examples of 28 28 gray-scale images of handwritten digits in K = 10 classes 0, 1, . . . , 9. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR-10 datasets, which have standard splits, but it does not explicitly state the train/validation/test split percentages or sample counts in the text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions PyTorch [19] and TensorFlow [1] as frameworks where SGDM is implemented, but it does not specify the version numbers of these or any other software dependencies used for the experiments. |
| Experiment Setup | Yes | For all algorithms, we use batch size s = 64 (and hence number batches per epoch is m = 1874), number of epochs T = 50. The regularization parameter is λ = 5 10 4. |