MIDAS is a differentiable neural architecture search method that replaces static architecture parameters with dynamic, input‑specific parameters generated through self‑attention. It localizes architecture selection to each spatial patch of the activation map and introduces a parameter‑free, topology‑aware search space that models two‑edge node topology. Evaluations on DARTS, NAS‑Bench‑201, and RDARTS show that MIDAS achieves state‑of‑the‑art accuracy on CIFAR‑10 and CIFAR‑100 and discovers optimal or near‑optimal architectures in benchmark spaces.
By Konstanty Subbotko
arXiv:2606. 04063v1 Announce Type: cross Abstract: Deploying large language models (LLMs) is challenging due to their significant memory and computational requirements.
By Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi, Phuong Hoai Ha
The paper introduces PPNAS, a prediction‑powered inference method for neural architecture search that combines a small set of accurately evaluated architectures with a large set of zero‑cost proxy (ZCP) evaluations. PPNAS uses the ordinal information from ZCPs to generate pairwise ranking supervision and applies a debiasing step to reconcile discrepancies between proxy and true performance rankings. Experiments show that PPNAS achieves state‑of‑the‑art results in predictor‑based NAS under limited evaluation budgets.
By Pascal Janetzky, Yuxin Wang, Michael Klar, Stefan Feuerriegel
arXiv:2608. 14472v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise.
By Abhishek Shukla, Ankur Sinha, Faiz Hamid
arXiv:2606. 07664v1 Announce Type: cross Abstract: Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms.
By Wenxiao Li, Yongjian Liu, Qing Xie
DIFFNAS is a new neural architecture search framework that eliminates the need for performance predictors by using a discrete graph diffusion model conditioned on zero‑cost performance proxies such as MeCo and SWAP. The method achieves state‑of‑the‑art results on NAS‑Bench‑201 for CIFAR‑10 and CIFAR‑100, matching optimal architectures while cutting search cost by up to 17.5× compared to DiffusionNAG. Although its accuracy on the MobileNetV3 search space is lower than DiffusionNAG, the results demonstrate the potential of diffusion models combined with training‑free metrics for scalable architecture search.
By Yize Sun, Hanzhang Ma, Zixin Wu, Xuechun Li, Volker Tresp, Yunpu Ma
arXiv:2603. 15106v2 Announce Type: replace Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requires DNN architectures to be specialized for each device separately.
By Mark Deutel, Simon Geis, Axel Plinge
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:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.
By Tyler King, Timothee Leleu
arXiv:2608. 14443v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) is naturally formulated as a bilevel optimization problem, where the upper-level optimizes the architecture using validation performance and the lower-level trains network parameters using training loss.
By Abhishek Shukla, Ankur Sinha, Faiz Hamid
The paper introduces a semi-automated method for structuring neural architecture search (NAS) using large language models (LLMs). It defines a high-level template of architectural attributes, which an LLM populates by analyzing research papers to create a diverse search space. The authors then propose FairNAD, a multi-type mutation strategy that combines fair idea sampling, Pareto-aware mutation, LLM-driven iterative mutation, and a fine-grained feedback loop to efficiently explore this space, achieving significant performance gains on CIFAR-10, CIFAR-100, and ImageNet16-120.
By Yuiko Sakuma, Masakazu Yoshimura, Marcel Gr\"opl, Zitang Sun, Junji Otsuka, Atsushi Irie, Takeshi Ohashi
arXiv:2605. 16138v2 Announce Type: replace Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost.
By Jason Weitz, Dmitri Demler, Benjamin Hawks, Aaron Wang, Nhan Tran, Javier Duarte