GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability

• URL: http://arxiv.org/abs/2011.07407v1
• Date: Sat, 14 Nov 2020 22:53:13 GMT
• Title: GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
• Authors: Daniel Lengyel, Janith Petangoda, Isak Falk, Kate Highnam, Michalis Lazarou, Arinbj\"orn Kolbeinsson, Marc Peter Deisenroth, Nicholas R. Jennings
• Abstract summary: We propose an efficient algorithm to visualise symmetries in neural networks. Our proposed method, GENNI, allows us to efficiently identify parameters that are functionally equivalent and then visualise the subspace of the resulting equivalence class.
• Score: 14.31120627384789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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