Lengyel, D;
Petangoda, JC;
Falk, I;
Highnam, K;
Lazarou, M;
Kolbeinsson, A;
Deisenroth, MP;
(2020)
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability.
arXiv: Ithaca, NY, USA.
Preview |
Text
2011.07407v1.pdf - Accepted Version Download (2MB) | Preview |
Abstract
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
| Type: | Working / discussion paper |
|---|---|
| Title: | GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://arxiv.org/abs/2011.07407v1 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117305 |
Archive Staff Only
 |
View Item |
