86 citations · 124 across the 9 of their papers we have counts for
9 papers
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…
IMEXnet: A Forward Stable Deep Neural Network
Eldad Haber, Keegan Lensink, Eran Treister +1
Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use. These challeng…
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Bo Chang, Minmin Chen, Eldad Haber +1
Recurrent neural networks have gained widespread use in modeling sequential data. Learning long-term dependencies using these models remains difficult though, due to exploding or v…
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…
The Discrete Adjoint Method for Exponential Integration
Kai Rothauge, Eldad Haber, Uri Ascher
The implementation of the discrete adjoint method for exponential time differencing (ETD) schemes is considered. This is important for parameter estimation problems that are constr…