DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions

21 Nov 2019Andrew HryniowskiAlexander Wong

From fully connected neural networks to convolutional neural networks, the learned parameters within a neural network have been primarily relegated to the linear parameters (e.g., convolutional filters). The non-linear functions (e.g., activation functions) have largely remained, with few exceptions in recent years, parameter-less, static throughout training, and seen limited variation in design... (read more)

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