@conference {1574, title = {Learning with a Wasserstein Loss}, booktitle = {Advances in Neural Information Processing Systems (NIPS 2015) 28}, year = {2015}, abstract = {

Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance. The Wasserstein distance provides a natural notion of dissimilarity for probability measures. Although optimizing with respect to the exact Wasserstein distance is costly, recent work has described a regularized approximation that is efficiently computed. We describe an efficient learning algorithm based on this regularization, as well as a novel extension of the Wasserstein distance from prob- ability measures to unnormalized measures. We also describe a statistical learning bound for the loss. The Wasserstein loss can encourage smoothness of the predic- tions with respect to a chosen metric on the output space. We demonstrate this property on a real-data tag prediction problem, using the Yahoo Flickr Creative Commons dataset, outperforming a baseline that doesn{\textquoteright}t use the metric.

}, url = {http://arxiv.org/abs/1506.05439}, author = {Charlie Frogner and Chiyuan Zhang and Hossein Mobahi and Mauricio Araya-Polo and Tomaso Poggio} } @conference {216, title = {Machine Learning Based Automated Fault Detection in Seismic Traces}, booktitle = {EAGE Conference and Exhibition 2014}, year = {2014}, month = {06/2014}, address = {The Netherlands}, abstract = {

Introduction:

The Initial stages of velocity model building (VMB) start off from smooth models that capture geological assumptions of the subsurface region under analysis. Acceptable velocity models result from successive iterations of human intervention (interpreter) and seismic data processing with in complex workflows. The interpreters ensure that any additions or corrections made by seismic processing are compliant with geological and geophysical knowledge. The information that seismic processing adds to the model consists of structural elements, faults are one of the most relevant of those events since they can signal reservoir boundaries or hydrocarbon traps. Faults are excluded in the initial models due to their local scale. Bringing faults into the model in early stages can help to steer the VMB process.

This work introduced a tool whose purpose is to assist the interpreters during the initial stages of the VMB, when no seismic data has been migrated. Our novel method is based on machine learning techniques and can automatically identify and localize faults from not migrated seismic data. Comprehensive research has targeted the fault localization problem, but most of the results are obtained using processed seismic data or images as input (Admasu and Toennies (2004); Tingdahl et al. (2001); Cohen et al. (2006); Hale (2013), etc). Our approach suggests an additional tool that can be used to speed up the
VMB process.

Fully automated VMB has not been achieved because the human factor is difficult to formalize in a way that can be systematically applied. Nonetheless, if our framework is extended to other seismic events or attributes, it might become a powerful tool to alleviate interpreters{\textquoteright} work.

}, url = {http://cbcl.mit.edu/publications/eage14.pdf}, author = {Chiyuan Zhang and Charlie Frogner and Mauricio Araya-Polo and Detlef Hohl} }