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.
Adam with model exponential moving average is effective for nonconvex optimization
Authors: Kwangjun Ahn, Ashok Cutkosky
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 25,463 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,942 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we offer a theoretical analysis of two modern optimization techniques for training large and complex models: (i) adaptive optimization algorithms, such as Adam, and (ii) the model exponential moving average (EMA). |
| Researcher Affiliation | Collaboration | Kwangjun Ahn Microsoft Research Cambridge, MA EMAIL Ashok Cutkosky Boston University Boston, MA EMAIL |
| Pseudocode | Yes | Algorithm 1 Discounted-to-nonconvex conversion (choosing increments via online learning) |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical experiments or datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical experiments or dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not involve empirical experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not involve empirical experiments, so no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments, so no experimental setup details like hyperparameters are provided. |