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SToG is an educational project for the course Bayesian Multimodeling by Eynullayev Altay, Rubtsov Denis, Firsov Sergey and Karpeev Gleb. SToG - is a library for feature selection using different stochastic gating approaches.

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Название исследуемой задачи:Мое название работы
Тип научной работы:M1P/НИР/CoIS
Автор:Имя Отчество Фамилия
Научный руководитель:степень, Фамилия Имя Отчество
Научный консультант(при наличии):степень, Фамилия Имя Отчество

Abstract

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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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SToG is an educational project for the course Bayesian Multimodeling by Eynullayev Altay, Rubtsov Denis, Firsov Sergey and Karpeev Gleb. SToG - is a library for feature selection using different stochastic gating approaches.

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