Artificial Neural Networks for In-silico Experiments on Perception. - Simon Odense (IJCCI (NCTA), 2015)

Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine. - Simon Odense; Roderick Edwards (J. Mach. Learn. Res., 2016)

Extracting M of N Rules from Restricted Boltzmann Machines. - Simon Odense; Artur S. d'Avila Garcez (ICANN (2), 2017)

Confidence Values and Compact Rule Extraction From Probabilistic Neural Networks. - Simon Odense; Artur S. d'Avila Garcez (NeSy, 2017)

Layerwise symbolic knowledge extraction from deep neural networks. - Simon Odense (2019)

Layerwise Knowledge Extraction from Deep Convolutional Networks. - Simon Odense; Artur S. d'Avila Garcez (CoRR, 2020)

Neural-Guided Runtime Prediction of Planners for Improved Motion and Task Planning with Graph Neural Networks. - Simon Odense; Kamal Gupta 001; William G. Macready (IROS, 2022)

Neural-Guided RuntimePrediction of Planners for Improved Motion and Task Planning with Graph Neural Networks. - Simon Odense; Kamal Gupta 001; William G. Macready (CoRR, 2022)

A Semantic Framework for Neural-Symbolic Computing. - Simon Odense; Artur d'Avila Garcez (CoRR, 2022)

A semantic framework for neurosymbolic computation. - Simon Odense; Artur d'Avila Garcez (Artif. Intell., 2025)