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..
Unchain the Search Space with Hierarchical Differentiable Architecture Search
Authors: Guanting Liu, Yujie Zhong, Sheng Guo, Matthew R. Scott, Weilin Huang8644-8652
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on CIFAR10 and Image Net demonstrate the effectiveness of the proposed HDAS. |
| Researcher Affiliation | Industry | Malong LLC EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Code is available at: https://github.com/msight-tech/research-HDAS |
| Open Datasets | Yes | We conduct experiments on CIFAR10 and Image Net. |
| Dataset Splits | Yes | In the search of cell-level structure, we follow DARTS (Liu, Simonyan, and Yang 2018) by using the same search space, hyperparameters and training scheme. [...] For training a single model, we use the same strategy and data processing methods as DARTS. More details can be found in SM. |
| Hardware Specification | No | No explicit mention of specific GPU/CPU models, processors, or detailed computer specifications used for running experiments was found. |
| Software Dependencies | No | No specific software versions or library dependencies were explicitly mentioned for reproducibility. |
| Experiment Setup | No | The paper mentions following the hyperparameters and training scheme of DARTS, but does not explicitly provide the specific values or details within the paper itself. |