4 citations · 17 across the 20 of their papers we have counts for
20 papers
Latent Space Inference via Paired Autoencoders
Emma Hart, Bas Peters, Julianne Chung +1
This work describes a novel data-driven latent space inference framework built on paired autoencoders to handle observational inconsistencies when solving inverse problems. Our app…
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems
Matthias Chung, Bas Peters, Michael Solomon
In this book chapter, we discuss recent advances in data-driven approaches for inverse problems. In particular, we focus on the \emph{paired autoencoder} framework, which has prove…
Machine Learning for Airborne Electromagnetic Data Inversion: a Bootstrapped Approach
Ophir Greif, Bas Peters, Michael S. McMillan +2
Aircraft-based surveying to collect airborne electromagnetic data is a key method to image large swaths of the Earth's surface in pursuit of better knowledge of aquifer systems. De…
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentat…
Inverting airborne electromagnetic data with machine learning
Michael S. McMillan, Bas Peters, Ophir Greif +2
This study focuses on inverting time-domain airborne electromagnetic data in 2D by training a neural-network to understand the relationship between data and conductivity, thereby r…
Paired Autoencoders for Likelihood-free Estimation in Inverse Problems
Matthias Chung, Emma Hart, Julianne Chung +2
We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to…
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
Rafael Orozco, Philipp Witte, Mathias Louboutin +4
InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributio…
CQnet: convex-geometric interpretation and constraining neural-network trajectories
Bas Peters
We introduce CQnet, a neural network with origins in the CQ algorithm for solving convex split-feasibility problems and forward-backward splitting. CQnet's trajectories are interpr…
Point-to-set distance functions for weakly supervised segmentation
Bas Peters
When pixel-level masks or partial annotations are not available for training neural networks for semantic segmentation, it is possible to use higher-level information in the form o…
Fully reversible neural networks for large-scale 3D seismic horizon tracking
Bas Peters, Eldad Haber
Tracking a horizon in seismic images or 3D volumes is an integral part of seismic interpretation. The last few decades saw progress in using neural networks for this task, starting…
Deep connections between learning from limited labels & physical parameter estimation -- inspiration for regularization
Bas Peters
Recently established equivalences between differential equations and the structure of neural networks enabled some interpretation of training of a neural network as partial-differe…
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface cha…
Symmetric block-low-rank layers for fully reversible multilevel neural networks
Bas Peters, Eldad Haber, Keegan Lensink
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 t…
Fully Hyperbolic Convolutional Neural Networks
Keegan Lensink, Bas Peters, Eldad Haber
Convolutional Neural Networks (CNN) have recently seen tremendous success in various computer vision tasks. However, their application to problems with high dimensional input and o…
Does shallow geological knowledge help neural-networks to predict deep units?
Bas Peters, Eldad Haber, Justin Granek
Geological interpretation of seismic images is a visual task that can be automated by training neural networks. While neural networks have shown to be effective at various interpre…
Neural-networks for geophysicists and their application to seismic data interpretation
Bas Peters, Eldad Haber, Justin Granek
Neural-networks have seen a surge of interest for the interpretation of seismic images during the last few years. Network-based learning methods can provide fast and accurate autom…
Generalized Minkowski sets for the regularization of inverse problems
Bas Peters, Felix J. Herrmann
Many works on inverse problems in the imaging sciences consider regularization via one or more penalty functions or constraint sets. When the models/images are not easily described…
Algorithms and software for projections onto intersections of convex and non-convex sets with applications to inverse problems
Bas Peters, Felix J. Herrmann
We propose algorithms and software for computing projections onto the intersection of multiple convex and non-convex constraint sets. The software package, called SetIntersectionPr…
Automatic classification of geologic units in seismic images using partially interpreted examples
Bas Peters, Justin Granek, Eldad Haber
Geologic interpretation of large seismic stacked or migrated seismic images can be a time-consuming task for seismic interpreters. Neural network based semantic segmentation provid…
Multi-resolution neural networks for tracking seismic horizons from few training images
Bas Peters, Justin Granek, Eldad Haber
Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, auto…