Unchain the Search Space with Hierarchical Differentiable Architecture Search
Authors: Guanting Liu, Yujie Zhong, Sheng Guo, Matthew R. Scott, Weilin Huang8644-8652
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {gualiu, jaszhong, sheng, mscott, whuang}@malongtech.com |
| 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. |