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Automated pipeline uncovers bias in MoE4 architecture search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an automated pipeline to explore heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR dataset ecosystem. This pipeline systematically combines base architecture families into MoE4 ensembles, utilizing a convolutional gating network with specific training techniques. A significant finding revealed a coverage bias in the search space, where alphabetical enumeration led to the exploration of only a single family, AirNet, instead of the intended broader combination. The study identified ShuffleNet and MobileNetV3 as high-accuracy contributors within the AirNet scope and suggested excluding FractalNet and MNASNet in future campaigns. AI

IMPACT Identifies a critical bias in automated architecture search, potentially improving future model development efficiency.

RANK_REASON The cluster contains a research paper detailing a systematic exploration of neural network architectures using an automated search pipeline. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Automated pipeline uncovers bias in MoE4 architecture search

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The cluster contains a research paper detailing a systematic exploration of neural network architectures using an automated search pipeline. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yashkumar R Lukhi, Harsh Rameshbhai Moradiya, Radu Timofte, Dmitry Ignatov ·

    Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search

    arXiv:2606.23739v1 Announce Type: new Abstract: We present an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem. Building on a hand-crafted heterogeneous MoE reference model, …

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