mlsanityData to 14 Oct 2026

On the space of coefficients of a Feed Forward Neural Network

arXiv 2109.03362 ↗First posted 7 Sep 2021cs.LG cs.NEcited by 2

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Abstract (arXiv)

We define and establish the conditions for `equivalent neural networks' - neural networks with different weights, biases, and threshold functions that result in the same associated function. We prove that given a neural network N with piece-wise linear activation, the space of coefficients describing all equivalent neural networks is given by a semialgebraic set. This result is obtained by studying different representations of a given piece-wise linear function using the Tarski-Seidenberg theorem.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 7 Sep 2021.

Builds on

10 of 24 references.

References · 10

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Semialgebraic Neural Networks: From roots to representationscited by 0
  2. 2025Forgetting is Everywherecited by 0