T
Theodore Lim
Researcher at Heriot-Watt University
Publications - 162
Citations - 4832
Theodore Lim is an academic researcher from Heriot-Watt University. The author has contributed to research in topics: Haptic technology & Virtual reality. The author has an hindex of 29, co-authored 157 publications receiving 4150 citations. Previous affiliations of Theodore Lim include University of Strathclyde.
Papers
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Journal ArticleDOI
An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games
Elizabeth Boyle,Thomas Hainey,Thomas Connolly,Grant Gray,Jeffrey Earp,Michela Ott,Theodore Lim,Manuel Ninaus,Cláudia Ribeiro,João Pereira +9 more
TL;DR: Future research on digital games would benefit from a systematic programme of experimental work, examining in detail which game features are most effective in promoting engagement and supporting learning.
Journal ArticleDOI
Mapping learning and game mechanics for serious games analysis
Sylvester Arnab,Theodore Lim,Maira B. Carvalho,Francesco Bellotti,Sara de Freitas,Sandy Louchart,Neil Suttie,Riccardo Berta,Alessandro De Gloria +8 more
TL;DR: The Learning Mechanics–Game Mechanics (LM-GM) model is proposed, which supports SG analysis and design by allowing reflection on the various pedagogical and game elements in an SG.
Proceedings Article
SMASH: One-Shot Model Architecture Search through HyperNetworks
TL;DR: A technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture is proposed, achieving competitive performance with similarly-sized hand-designed networks.
Posted Content
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
TL;DR: Methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification are presented.
Proceedings ArticleDOI
Neural photo editing with introspective adversarial networks
TL;DR: The Neural Photo Editor is presented, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images, and the Introspective Adversarial Network is introduced, a novel hybridization of the VAE and GAN.