Grid-Functioned Neural Networks

Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

2 Citations (SciVal)
39 Downloads (Pure)


We introduce a new neural network architecture that we call "grid-functioned" neural networks. It utilises a grid structure of network parameterisations that can be specialised for different subdomains of the problem, while maintaining smooth, continuous behaviour. The grid gives the user flexibility to prevent gross features from overshadowing important minor ones. We present a full characterisation of its computational and spatial complexity, and demonstrate its potential, compared to a traditional architecture, over a set of synthetic regression problems. We further illustrate the benefits through a real-world 3D skeletal animation case study, where it offers the same visual quality as a state-of-the-art model, but with lower computational complexity and better control accuracy.
Original languageEnglish
Title of host publicationProceedings of the Thirty-eighth International Conference on Machine Learning (ICML 2021)
PublisherCurran Associates, Inc.
Number of pages9
Publication statusPublished - 18 Jul 2021
EventThirty-eighth International Conference on Machine Learning - Virutal only
Duration: 18 Jul 202124 Jul 2021
Conference number: 38

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


ConferenceThirty-eighth International Conference on Machine Learning
Abbreviated titleICML
Internet address


  • Machine Learning
  • Neural Networks


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