The author presents the DNA family models as a solution to the ineffectiveness of weight-sharing NAS due to an oversized search space, offering scalability, efficiency, and multi-modal compatibility. The approach involves modularizing the search space into blocks and employing distilling neural architecture techniques.
The author introduces TransNAS-TSAD, a framework that combines transformer architecture with neural architecture search (NAS) to enhance anomaly detection in time series data. The approach focuses on balancing computational efficiency with detection accuracy.
Transitioning to efficient anytime NAS for tabular data with ATLAS.
Automated search for efficient RNN architectures with reduced computational demand.
TransNAS-TSADは、トランスフォーマー・アーキテクチャを活用した多目的ニューラルアーキテクチャ検索により、時系列異常検出の能力を高める。
Addressing the challenges of robust architectures in NAS through benchmarking and theoretical insights.
DiffusionNAG proposes a paradigm shift in Neural Architecture Generation by leveraging diffusion models to efficiently generate task-optimal architectures guided by property predictors.
DNA family models improve weight-sharing NAS efficiency and effectiveness by modularizing the search space into blocks and utilizing distilling neural architecture techniques.
Die DNA-Familie verbessert das Weight-Sharing NAS durch blockweise Überwachung.
ECToNAS는 가벼우면서 비용 효율적인 진화적 교차 토폴로지 신경 아키텍처 검색 알고리즘입니다.