
Simulating Continuum Mechanics with MultiScale Graph Neural Networks
Continuum mechanics simulators, numerically solving one or more partial ...
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Comparing recurrent and convolutional neural networks for predicting wave propagation
Dynamical systems can be modelled by partial differential equations and ...
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Approximating the solution to wave propagation using deep neural networks
Humans gain an implicit understanding of physical laws through observing...
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Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling
We review some of the latest approaches to analysing cardiac electrophys...
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Inverting The Generator Of A Generative Adversarial Network (II)
Generative adversarial networks (GANs) learn a deep generative model tha...
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Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
We propose a novel deep learning model for classifying medical images in...
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Conditional Autoencoders with Adversarial Information Factorization
Generative models, such as variational autoencoders (VAE) and generativ...
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LatentPoison  Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined...
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Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep repre...
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On denoising autoencoders trained to minimise binary crossentropy
Denoising autoencoders (DAEs) are powerful deep learning models used for...
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A data augmentation methodology for training machine/deep learning gait recognition algorithms
There are several confounding factors that can reduce the accuracy of ga...
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Task Specific Adversarial Cost Function
The cost function used to train a generative model should fit the purpos...
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Appearancebased indoor localization: A comparison of patch descriptor performance
Vision is one of the most important of the senses, and humans use it ext...
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Anil A Bharath
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