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].
Fine-grained Optimization of Deep Neural Networks
Authors: Mete Ozay
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental analyses show that image classification accuracy of baseline DNNs can be boosted using FG-SGD on collections of manifolds identified by multiple constraints. Due to page limit, experimental analyses are given in the supplemental material. In these analyses, we observe that our proposed methods can improve convergence properties and classification performance of CNNs. |
| Researcher Affiliation | Academia | EMAIL. The paper provides a personal email address but lacks clear institutional names (university or company) to definitively classify the author's affiliation type. Given the conference (NeurIPS), it is likely academic, but explicit institutional names are not present. |
| Pseudocode | Yes | Algorithm 1 Optimization using FG-SGD on products manifolds of fine-grained weights. |
| Open Source Code | No | The paper does not provide any statement about releasing source code, nor does it include a link to a code repository. |
| Open Datasets | No | The paper refers to "various datasets" and mentions ImageNet in the references, but it does not explicitly state which datasets were used for its experiments within the main text or provide concrete access information (link, DOI, formal citation in context) for any public dataset it utilized. |
| Dataset Splits | No | The paper mentions "training set" and states "Implementation details and experimental analyses are given in the supp. mat.", but it does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts) in the main text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) that would be needed to reproduce the experiments. |
| Experiment Setup | No | The paper mentions "Θ (set of hyperparameters)" as an input to its algorithm but does not provide specific values for these hyperparameters or other concrete details about the experimental setup or training configurations in the main text. |