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 [1].
Random Scaling and Momentum for Non-smooth Non-convex Optimization
Authors: Qinzi Zhang, Ashok Cutkosky
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate this finding empirically, we implemented the SGDM algorithm with random scaling and assessed its performance against the standard SGDM optimizer without random scaling. Our evaluation involved the ResNet-18 model (He et al., 2016) on the CIFAR-10 image classification benchmark (Krizhevsky & Hinton, 2009). |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Boston University, Boston, USA. |
| Pseudocode | Yes | Algorithm 1 O2NC (Cutkosky et al., 2023) and Algorithm 2 Exponentiated O2NC |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Our evaluation involved the ResNet-18 model (He et al., 2016) on the CIFAR-10 image classification benchmark (Krizhevsky & Hinton, 2009). |
| Dataset Splits | No | The paper mentions using the CIFAR-10 dataset and reports train and test metrics, but does not explicitly specify the data splits (e.g., percentages or counts for training, validation, and testing sets) or refer to standard predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For the hyperparameters, we configured the learning rate at 0.01, the momentum constant at 0.9, and the weight decay at 5e-4. |