REVIEW 6 cited by
A Survey on Neural Architecture Search
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search. The choice of the network architecture has proven to be critical, and many advances in deep learning spring from its immediate improvements. However, deep learning techniques are computationally intensive and their application requires a high level of domain knowledge. Therefore, even partial automation of this process helps to make deep learning more accessible to both researchers and practitioners. With this survey, we provide a formalism which unifies and categorizes the landscape of existing methods along with a detailed analysis that compares and contrasts the different approaches. We achieve this via a comprehensive discussion of the commonly adopted architecture search spaces and architecture optimization algorithms based on principles of reinforcement learning and evolutionary algorithms along with approaches that incorporate surrogate and one-shot models. Additionally, we address the new research directions which include constrained and multi-objective architecture search as well as automated data augmentation, optimizer and activation function search.
Forward citations
Cited by 6 Pith papers
-
RELO: Reinforcement Learning to Localize for Visual Object Tracking
RELO replaces handcrafted spatial priors with a reinforcement learning policy for target localization in visual tracking and reports 57.5% AUC on LaSOText without template updates.
-
MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks
MoENAS, a mixture-of-experts neural architecture search, produces MobileViTv2 variants with reported accuracy, fairness, robustness, and generalization gains over state-of-the-art edge DNNs on person classification.
-
XferNAS: Transfer Neural Architecture Search
XferNAS transfers knowledge across neural architecture searches to reduce search time by a factor of 33 on CIFAR-10/100 while achieving new records of 1.99% and 14.06% error.
-
PhaseNAS: Language-Model Driven Architecture Search with Dynamic Phase Adaptation
PhaseNAS uses dynamic small-to-large LLM switching and a template language to search neural architectures, claiming better accuracy and lower search cost on NAS-Bench-Macro, CIFAR, and COCO.
-
Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation
Self-adaptive 2D-3D FCN ensemble optimized by multiobjective evolution for prostate segmentation on PROMISE12 achieves top-10 ranking with smaller size than prior auto-designed models.
-
Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search
Bi-NAS applies bi-level NAS to search explanation architectures and LLMs for text generation, reporting gains in both recommendation accuracy and explanation effectiveness across four real-world datasets.
Discussion (0). Continue with ORCID to comment.