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Found 7 papers, 2 papers with code
Cascade Learning Localises Discriminant Features in Visual Scene Classification
In this work, we investigate the localisation of features learned via two varied learning paradigms and demonstrate the superiority of one learning approach with respect to localisation.
Improving the Robustness of Neural Multiplication Units with Reversible Stochasticity
Multilayer Perceptrons struggle to learn certain simple arithmetic tasks.
Data-Driven Interpolation for Super-Scarce X-Ray Computed Tomography
To do so, we train shallow neural networks to combine two neighbouring acquisitions into an estimated measurement at an intermediate angle.
Modelling nonlinear dependencies in the latent space of inverse scattering
The conducted meta-analysis also shows a clear practical advantage of such constructed generative models in terms of the efficiency of their training process compared to existing generative models for images.
Sinogram Enhancement with Generative Adversarial Networks using Shape Priors
Compensating scarce measurements by inferring them from computational models is a way to address ill-posed inverse problems.
Learning Division with Neural Arithmetic Logic Modules
To achieve systematic generalisation, it first makes sense to master simple tasks such as arithmetic.
A Primer for Neural Arithmetic Logic Modules
Neural Arithmetic Logic Modules have become a growing area of interest, though remain a niche field.
