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..
Towards modular and programmable architecture search
Authors: Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon, Darshan Patil, Nghia Le, Daniel Ferreira
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experiments. We showcase the modularity and programmability of our language by running experiments that rely on decoupled of search spaces and search algorithms. Table 1: Test results for search space experiments. Table 2: Test results for search algorithm experiments. |
| Researcher Affiliation | Collaboration | Carnegie Mellon University1, TU Wien2, Microsoft Research Montreal3 |
| Pseudocode | Yes | Algorithm 1: Transition. Algorithm 2: Ordered Hyperps. Algorithm 3: Random search. |
| Open Source Code | Yes | We release an implementation of our language with this paper2. 2Visit https://github.com/negrinho/deep_architect for code and documentation. |
| Open Datasets | Yes | We refer to the search spaces we consider as Nasbench [27], Nasnet [28], Flat [15], and Genetic [26]. |
| Dataset Splits | Yes | The test results for the fully trained architecture with the best validation accuracy are reported in Table 1. |
| Hardware Specification | Yes | We thank Google for generous TPU and GCP grants. |
| Software Dependencies | No | The paper mentions software like TensorFlow [24], PyTorch [25], and Scikit-Learn [22] but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | For the search phase, we randomly sample 128 architectures from each search space and train them for 25 epochs with Adam with a learning rate of 0.001. |