# GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability

arXiv: https://arxiv.org/abs/2011.07407 · HTML: https://mlsanity.com/paper/2011.07407/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances](https://mlsanity.com/paper/2105.12221/) (2021)
- [Symmetry in Neural Network Parameter Spaces](https://mlsanity.com/paper/2506.13018/) (2025)
- [Self-supervision of wearable sensors time-series data for influenza detection](https://mlsanity.com/paper/2112.13755/) (2021)
- [On the Subspace Structure of Gradient-Based Meta-Learning](https://mlsanity.com/paper/2207.03804/) (2022)
- [Symmetry-Aware Fully-Amortized Optimization with Scale Equivariant Graph Metanetworks](https://mlsanity.com/paper/2510.08300/) (2025)
- [Symmetry-Aware Graph Metanetwork Autoencoders: Model Merging through Parameter Canonicalization](https://mlsanity.com/paper/2511.12601/) (2025)

## Abstract (arXiv)

We propose an efficient algorithm to visualise symmetries in neural networks. Typically, models are defined with respect to a parameter space, where non-equal parameters can produce the same input-output map. Our proposed method, GENNI, allows us to efficiently identify parameters that are functionally equivalent and then visualise the subspace of the resulting equivalence class. By doing so, we are now able to better explore questions surrounding identifiability, with applications to optimisation and generalizability, for commonly used or newly developed neural network architectures.