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New ONNX-Net system enables universal neural architecture representation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ONNX-Net, a novel approach to create universal representations for neural architectures, aiming to overcome the limitations of existing methods tied to specific search spaces. This system utilizes ONNX files to represent diverse neural networks in a unified format, allowing for a single performance predictor to generalize across various architectures. The text-based encoding can accommodate arbitrary layer types and parameters, enabling instant architecture evaluation with strong zero-shot performance. AI

IMPACT Enables faster and more flexible evaluation of neural network architectures across diverse search spaces.

RANK_REASON The cluster contains an academic paper detailing a new method for neural architecture representation and performance prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ONNX-Net system enables universal neural architecture representation

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The cluster contains an academic paper detailing a new method for neural architecture representation and performance prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shiwen Qin, Alexander Auras, Shay B. Cohen, Elliot J. Crowley, Michael Moeller, Linus Ericsson, Jovita Lukasik ·

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

    arXiv:2510.04938v2 Announce Type: replace-cross Abstract: Neural architecture search (NAS) automates the design process of high-performing architectures, but remains bottlenecked by expensive performance evaluation. Most existing studies that achieve faster evaluation are mostly …

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