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..
Meta Architecture Search
Authors: Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that on Imagenet classification, we can find a model that achieves 25.7% top-1 error and 8.1% top-5 error by adapting the architecture in less than an hour from an 8 GPU days pretrained meta-network. We believe our framework will open up new possibilities for efficient and massively scalable architecture search research across multiple tasks. |
| Researcher Affiliation | Collaboration | Albert Shaw1 Wei Wei2 Weiyang Liu1 Le Song1,3 Bo Dai1,2 1Georgia Institute of Technology 2Google Research 3Ant Financial |
| Pseudocode | Yes | Algorithm 1 Bayesian meta Architecture SEarch (BASE) |
| Open Source Code | Yes | The code repository is available at https://github.com/ashaw596/meta_architecture_search. |
| Open Datasets | Yes | We show that on Imagenet classification, we can find a model that achieves 25.7% top-1 error and 8.1% top-5 error by adapting the architecture in less than an hour from an 8 GPU days pretrained meta-network. |
| Dataset Splits | No | To train our meta-network over a wide distribution of tasks with different image sizes, we define a new space of classification tasks by randomly selecting 10 Imagenet [7] classes and downsampling the images to 32 32, 64 64, or 224 224 image sizes. |
| Hardware Specification | Yes | All experiments were conducted with Nvidia 1080 Ti GPUs. |
| Software Dependencies | No | No specific versions of software dependencies (e.g., Python, PyTorch, TensorFlow, CUDA) are mentioned, only general algorithmic components like Gumbel-Softmax. |
| Experiment Setup | Yes | The meta-network was trained for 130 epochs. At each epoch, we sampled and trained on a total of 24 tasks, sampling 8 10-class discrimination tasks each from Imagenet32, Imagenet64, and Imagenet224. |