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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation
Authors: Yang Zhao, Hao Zhang
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Yang Zhao & Hao Zhang Department of Electronic Engineering Tsinghua University EMAIL, EMAIL |
| Pseudocode | No | The paper describes computational steps and flow, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | For experiments, we use the VGG16 network architecture to perform the image classification task on the Image Net dataset. |
| Dataset Splits | No | The paper refers to 'training set' and 'test set' but does not explicitly provide details about a validation set or specific split percentages for reproduction. |
| Hardware Specification | Yes | All implementations are deployed on the Nvidia-A100 station with a batch size of 512. |
| Software Dependencies | No | The paper mentions using an 'SGD optimizer' but does not provide specific software names with version numbers for reproducibility (e.g., Python, PyTorch/TensorFlow versions, or other libraries). |
| Experiment Setup | Yes | The hyper-parameters are the same with them in the paper (Simonyan & Zisserman, 2014). Also, similar to the setting in the paper, all the images are simply resized to 224 224. |