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..
On Redundancy and Diversity in Cell-based Neural Architecture Search
Authors: Xingchen Wan, Binxin Ru, Pedro M Esperança, Zhenguo Li
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we conduct an empirical post-hoc analysis of architectures from the popular cellbased search spaces and find that the existing search spaces contain a high degree of redundancy |
| Researcher Affiliation | Collaboration | 1Machine Learning Research Group, University of Oxford 2Huawei Noah s Ark Lab, London 3Huawei Noah s Ark Lab, Hong Kong |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | Code is available at https: //github.com/xingchenwan/cell-based-NAS-analysis. |
| Open Datasets | Yes | NB301 (Siems et al., 2020) which includes 50,000+ architecture performance pairs in the DARTS space |
| Dataset Splits | Yes | on the CIFAR-10 dataset using the standard train/val split |
| Hardware Specification | Yes | on a single NVIDIA Tesla V100 GPU |
| Software Dependencies | No | The paper lists training parameters and techniques like "Optimizer: SGD", "Cutout: True", and "Mixup: True", but does not specify versions for any software libraries or dependencies (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | Specifically, we train architectures obtained from stacking the cells 8 times (8-layer architectures) with initial channel count of 32 on the CIFAR-10 dataset using the standard train/val split, and we use the hyperparameters below on a single NVIDIA Tesla V100 GPU: Optimizer: SGD Initial learning rate: 0.025 Final learning rate: 1e-8 Learning rate schedule: cosine annealing Epochs: 100 Weight decay: 3e-4 Momentum: 0.9 Auxiliary tower: True Auxliary weight: 0.4 Cutout: True Cutout length: 16 Drop path probability: 0.2 Gradient clip: 5 Batch size: 96 Mixup: True Mixup alpha: 0.2 |