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Continuous Learned Primal Dual
Neural ordinary differential equations (Neural ODEs) propose the idea that a sequence of layers in a neural network is just a discretisation of an ODE, and thus can instead be directly modelled by a parameterised ODE.
Continuous U-Net: Faster, Greater and Noiseless
Despite the powerful performance reported by existing U-Net type networks, they suffer from several major limitations.
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network.
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction
We demonstrate that purely learning on softmax inputs in combination with scarce training data yields overfitting as the network learns the inputs by heart.
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