Architecture self-attention mechanism: nonlinear optimization for neural architecture search
Bibliographic record
Abstract
Neural Architecture Search (NAS) is a very prevalent method of automatically designing neural network architectures.It has recently drawn considerable attention since it relieves the manual design labour of neural networks.However, existing NAS methods ignore the interrelationships among candidate architectures in the search space.As a consequence, the objective neural architecture extracted from the search space suffers from performance unstable due to the interrelationship collapse.In this paper, we propose architecture self-attention mechanism for neural architecture search (ASM-NAS) to address the above problem.Specifically, the proposed architecture self-attention mechanism constructs the interrelationships among architectures by interacting information between any two candidate architectures.Through learning the interrelationships, it selectively emphasizes some architectures important to the network while suppressing unimportant ones, which provides significant references for the architecture selection.Therefore, we improves the performance stability of the architecture search by the above startegy.Besides, our proposed method is high-efficiency and executes architecture search with low time and space costs.Compared to other advanced NAS approaches, our ASM-NAS is able to achieve better architecture search performance on the image classification datasets of CIFAR10, CIFAR100, fashionMNIST and ImageNet.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".