Generative Adversarial Neural Architecture Search with Importance Sampling
Why this work is in the frame
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Bibliographic record
Abstract
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess. The variation in search spaces adopted has further affected a fair comparison between search strategies. In this paper, we focus on search strategies in NAS and propose Generative Adversarial NAS (GA-NAS), promoting stable and reproducible neural architecture search. GA-NAS is theoretically inspired by importance sampling for rare event simulation, and iteratively refits a generator to previously discovered top architectures, thus increasingly focusing on important parts of the search space. We propose an efficient adversarial learning approach in GA-NAS, where the generator is not trained based on a large number of observations on architecture performance, but based on the relative prediction made by a discriminator, thus significantly reducing the number of evaluations required. Extensive experiments show that GA-NAS beats the best published results under several cases on the public NAS benchmarks including NAS-Bench-101, NAS-Bench-201, and NAS-Bench-301. We further show that GA-NAS can handle ad-hoc search constraints and search spaces. GA-NAS can find new architectures that enhance EfficientNet and ProxylessNAS in terms of ImageNet Top-1 accuracy and/or the number of parameters by searching in their original search spaces.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it