Volume-preserving Neural NetworksAndrew GodboutBryn GillcashGordon MacDonaldStephanie Cairns
Recently, flow models parameterized by neural networks have been used to design efficient Markov chain Monte Carlo (MCMC) transition kernels. However, inefficient utilization of gradient information of the target distribution or the use of volume-preserv
More extensions can be found in [21] for fractional diffusion equation, in [22] for stochastic differential equations, and in [23] using deep neural networks trained by multi-fidelity data. For the classical numerical methods, it is necessary to carefully design stable or structure preserving sch...
Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation due to its accuracy and efficiency. CNNs are generally trained with loss functions that measure the segmentation accuracy, such as the cross entropy loss and Dice loss. However, lesion load ...
Neural networks applicationsSelf-organising mapsData series analysis and clusteringIn this paper, we present a validation study for volume preserving non-rigid registration of 3D contrast-enhanced magnetic resonance mammograms. This study allows for the first time to assess the effectiveness of a volume...
The accuracy of soft computing (SC) techniques is evaluated using Fuzzy Systems, Evolutionary computing, Neural Networks, Machine Learning and probabilistic Reasoning.Mandapati, SridharBhogapathi, Raveendra Babu
Moreover, to be different from many variational based image segmentation algorithms, the proposed algorithm can be directly unrolled to a new Volume Preserving and TV regularized softmax (VPTV-softmax) layer for semantic segmentation in the popular Deep Convolution Neural Network (DCNN). The ...