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.
Stochastic Sign Descent Methods: New Algorithms and Better Theory
Authors: Mher Safaryan, Peter Richtarik
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,579 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,331 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 | We validate several aspects of our theoretical findings with numerical experiments. 5. Experiments We verify several aspects of our theoretical results experimentally using the MNIST dataset with feed-forward neural network (FNN) and the well known Rosenbrock (nonconvex) function with d = 10 variables: |
| Researcher Affiliation | Academia | Mher Safaryan 1 Peter Richtárik 1 2 1KAUST, Saudi Arabia 2MIPT, Russia. |
| Pseudocode | Yes | Algorithm 1 SIGNSGD, Algorithm 2 PARALLEL SIGNSGD W/ MAJORITY VOTE, Algorithm 3 SSDM |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | using the MNIST dataset with feed-forward neural network (FNN) |
| Dataset Splits | No | The paper mentions 'train and test accuracies' and discusses training and testing, but it does not explicitly state the use or size of a validation set or split. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or cloud instance types used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | First column shows train and test accuracies with mini-batch size 128 and averaged over 3 repetitions. We first tuned the constant step size over logarithmic scale {1, 0.1, 0.01, 0.001, 0.0001} and then fine tuned it. Left plot used constant step size γ = 0.02, right plot used variable step size with γ0 = 0.02. We set mini-batch size 1 and used the same initial point. |