Search Results for author: Mikel Luján

Found 3 papers, 3 papers with code

A Unified Theory of Diversity in Ensemble Learning

1 code implementation • 10 Jan 2023 • Danny Wood, Tingting Mu, Andrew Webb, Henry Reeve, Mikel Luján, Gavin Brown

We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios.

Ensemble Learning Open-Ended Question Answering

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Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality

2 code implementations • 20 Aug 2018 • Sajad Saeedi, Bruno Bodin, Harry Wagstaff, Andy Nisbet, Luigi Nardi, John Mawer, Nicolas Melot, Oscar Palomar, Emanuele Vespa, Tom Spink, Cosmin Gorgovan, Andrew Webb, James Clarkson, Erik Tomusk, Thomas Debrunner, Kuba Kaszyk, Pablo Gonzalez-de-Aledo, Andrey Rodchenko, Graham Riley, Christos Kotselidis, Björn Franke, Michael F. P. O'Boyle, Andrew J. Davison, Paul H. J. Kelly, Mikel Luján, Steve Furber

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge.

Autonomous Vehicles

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Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

3 code implementations • 8 Oct 2014 • Luigi Nardi, Bruno Bodin, M. Zeeshan Zia, John Mawer, Andy Nisbet, Paul H. J. Kelly, Andrew J. Davison, Mikel Luján, Michael F. P. O'Boyle, Graham Riley, Nigel Topham, Steve Furber

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging.

Benchmarking

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