← Back to all news
arXiv Machine Learning By Yize Sun, Hanzhang Ma, Zixin Wu, Xuechun Li, Volker Tresp, Yunpu Ma

DIFFNAS: Predictor-Free Neural Architecture Search via Diffusion Model and Zero-Cost Evaluation

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 27

ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architectures

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
efficiencybenchmarks
More like this →
arXiv Machine Learning
Jul 14

HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS

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
More like this →