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Full-Text Articles in Physical Sciences and Mathematics
Normalization Techniques For Sequential And Graphical Data, Cole Pospisil
Normalization Techniques For Sequential And Graphical Data, Cole Pospisil
Theses and Dissertations--Mathematics
Normalization methods have proven to be an invaluable tool in the training of deep neural networks. In particular, Layer and Batch Normalization are commonly used to mitigate the risks of exploding and vanishing gradients. This work presents two methods which are related to these normalization techniques. The first method is Batch Normalized Preconditioning (BNP) for recurrent neural networks (RNN) and graph convolutional networks (GCN). BNP has been suggested as a technique for Fully Connected and Convolutional networks for achieving similar performance benefits to Batch Normalization by controlling the condition number of the Hessian through preconditioning on the gradients. We extend …
Batch Normalization Preconditioning For Neural Network Training, Susanna Luisa Gertrude Lange
Batch Normalization Preconditioning For Neural Network Training, Susanna Luisa Gertrude Lange
Theses and Dissertations--Mathematics
Batch normalization (BN) is a popular and ubiquitous method in deep learning that has been shown to decrease training time and improve generalization performance of neural networks. Despite its success, BN is not theoretically well understood. It is not suitable for use with very small mini-batch sizes or online learning. In this work, we propose a new method called Batch Normalization Preconditioning (BNP). Instead of applying normalization explicitly through a batch normalization layer as is done in BN, BNP applies normalization by conditioning the parameter gradients directly during training. This is designed to improve the Hessian matrix of the loss …
Lattice Simplices: Sufficiently Complicated, Brian Davis
Lattice Simplices: Sufficiently Complicated, Brian Davis
Theses and Dissertations--Mathematics
Simplices are the "simplest" examples of polytopes, and yet they exhibit much of the rich and subtle combinatorics and commutative algebra of their more general cousins. In this way they are sufficiently complicated --- insights gained from their study can inform broader research in Ehrhart theory and associated fields.
In this dissertation we consider two previously unstudied properties of lattice simplices; one algebraic and one combinatorial. The first is the Poincar\'e series of the associated semigroup algebra, which is substantially more complicated than the Hilbert series of that same algebra. The second is the partial ordering of the elements of …