ONNX-Net introduces a universal representation for neural architectures using natural language descriptions, enabling instant performance prediction across diverse search spaces. The authors present ONNX-Bench, a benchmark of over 600k architecture–accuracy pairs compiled from open‑source NAS‑bench networks in ONNX format. Experiments demonstrate strong zero‑shot predictive performance with minimal pretraining, overcoming the limitations of cell‑based, graph‑encoded approaches.
By Shiwen Qin, Alexander Auras, Shay B. Cohen, Elliot J. Crowley, Michael Moeller, Linus Ericsson, Jovita Lukasik
CoRA-NAS is a two‑stage neural architecture search framework that first uses a static coarse ranking (CoRA‑Rank) based on capacity and structure‑at‑initialization proxies, then refines this ranking with low‑cost learning‑curve extrapolation (CoRA‑Refine) using an ExtraTrees model. The method achieves high Spearman correlations across multiple benchmark spaces and selects architectures with accuracy close to the ground‑truth best, all while using only about 1% of the training cost of fully training the candidate set. CoRA‑NAS provides a single configuration that works across different search spaces, combining cross‑space ranking robustness with efficient architecture selection.
CoRA-NAS is a two‑stage neural architecture search framework that first uses a static coarse ranking (CoRA‑Rank) based on capacity and structure‑at‑initialization proxies, then refines this ranking with low‑cost learning‑curve extrapolation (CoRA‑Refine) using anchor samples and an ExtraTrees residual model. The method achieves high Spearman correlations across multiple NAS benchmarks and selects architectures that approach the best‑known accuracy with only about 1% of the training cost, without relying on fully trained labels for ranking. It demonstrates robust cross‑space performance and improves over static capacity proxies, especially in size‑only search spaces.
By Yifan Yang, Zhaoyan Wang, Zheng Gao, Xiaoyu Li, Jiaojiao Jiang
arXiv:2403. 10318v3 Announce Type: replace Abstract: Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context size.
By Naili Xing, Shaofeng Cai, Lingze Zeng, Jiaqi Zhu, Peng Lu, Jian Pei, Beng Chin Ooi
arXiv:2607. 11746v1 Announce Type: new Abstract: With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential.
By Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma, Judeson Anthony Fernando, Lukas Frickenstein, Pierpaolo Mori, Manoj Rohit Vemparala, Alexander Frickenstein, Walter Stechele
RiPPLE is a method for ranking neural architectures across an entire search space using only a small fraction of early training data. It treats partial training as labels for a limited set of anchor architectures, extrapolates their learning curves, and propagates these surrogate labels to other architectures without requiring per‑candidate features. The approach is evaluated on twelve benchmark cells from four search‑space families and the larger DARTS space, demonstrating its effectiveness in ranking quality, label efficiency, and architecture selection.
By Yifan Yang, Zhaoyan Wang, Zheng Gao, Xiaoyu Li, Jiaojiao Jiang