Search Results for author:
Found 10 papers, 0 papers with code
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
InvertibleNetworks. jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions.
CQnet: convex-geometric interpretation and constraining neural-network trajectories
We introduce CQnet, a neural network with origins in the CQ algorithm for solving convex split-feasibility problems and forward-backward splitting.
Point-to-set distance functions for weakly supervised segmentation
We illustrate the capabilities in case of a) one or more classes do not have any annotation; b) there is no annotation at all; c) there are bounding boxes.
Deep connections between learning from limited labels & physical parameter estimation -- inspiration for regularization
We show that explicit regularization of model parameters in PDE constrained optimization translates to regularization of the network output.
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface characterization.
Symmetric block-low-rank layers for fully reversible multilevel neural networks
Factors that limit the size of the input and output of a neural network include memory requirements for the network states/activations to compute gradients, as well as memory for the convolutional kernels or other weights.
Fully Hyperbolic Convolutional Neural Networks
However, their application to problems with high dimensional input and output, such as high-resolution image and video segmentation or 3D medical imaging, has been limited by various factors.
Neural-networks for geophysicists and their application to seismic data interpretation
Neural-networks have seen a surge of interest for the interpretation of seismic images during the last few years.
Automatic classification of geologic units in seismic images using partially interpreted examples
Tests on seismic images and interpretation information from the Sea of Ireland show that we obtain high-quality predicted interpretations from a small number of large seismic images.
Multi-resolution neural networks for tracking seismic horizons from few training images
Our networks learn from a small number of large seismic images without creating patches.
