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GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
Arinbjörn Kolbeinsson · Nicholas Jennings · Marc Deisenroth · Daniel Lengyel · Janith Petangoda · Michalis Lazarou · Kate Highnam · John IF Falk

Fri Dec 11 08:30 AM -- 09:30 AM (PST) @ None

In this paper, we propose an efficient algorithm to visualise symmetries in neural networks. Typically the models are defined with respect to a parameter space, where non-equal parameters can produce the same function. Our proposed tool, GENNI, allows us to identify parameters that are functionally equivalent and to then visualise the subspace of the resulting equivalence class. Specifically, we experiment on simple cases, to demonstrate how to identify and provide possible solutions for more complicated scenarios.

Author Information

Arinbjörn Kolbeinsson (Imperial College London)
Nicholas Jennings (Imperial College, London)
Marc Deisenroth (University College London)
Marc Deisenroth

Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book [Mathematics for Machine Learning](https://mml-book.github.io) published by Cambridge University Press (2020).

Daniel Lengyel (Imperial College London)
Janith Petangoda (Imperial College London)
Michalis Lazarou (Imperial College London)
Kate Highnam (Imperial College London)
John IF Falk (UCL)

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