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deccc47 · May 4, 2025

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Название исследуемой задачи:Использование предикаторной функции для построения ансамбля нейросетей
Тип научной работы:M1P
Автор:Уденеев Александр Владимирович
Научный руководитель:Бахтеев Олег
Научный консультант(при наличии):Бабкин Петр

Abstract

The automated search for optimal neural network architectures (NAS) is a challenging compu- tational problem, and Neural Ensemble Search (NES) is even more complex. In this work, we propose a surrogate-based approach for ensebmle creation. Neural architectures are represented as graphs, and their predictions on a dataset serve as training data for the surrogate function. Using this function, we develop an efficient NES framework that enables the selection of diverse and high-performing architectures. The resulting ensemble achieves superior predictive accuracy on CIFAR-10 compared to other one-shot NES methods, demonstrating the effectiveness of our approach.

Keywords: NES, GCN, triplet loss, surrogate function

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Software modules developed as part of the study

  1. A python package mylib with all implementation here.
  2. A code with all experiment visualisation here. Can use colab.

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