◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

E. Laloy

10 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author4
  • last author1

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • physics.geo-ph3
  • cs.CV2
  • cs.LG1
  • eess.IV1
  • physics.comp-ph1
  • physics.data-an1

identity via Semantic Scholar / OpenAlex

activity
20172021
most citedInversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

271 citations · 282 across the 5 of their papers we have counts for

collaborators
Showing physics.geo-phShow all

3 papers · 1 filter

physics.geo-ph2021

Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors

M. Amaya, N. Linde, E. Laloy

Bayesian model selection enables comparison and ranking of conceptual subsurface models described by spatial prior models, according to the support provided by available geophysica…

physics.geo-ph2019

Approaching geoscientific inverse problems with vector-to-image domain transfer networks

Eric Laloy, Niklas Linde, Diederik Jacques

We present vec2pix, a deep neural network designed to predict categorical or continuous 2D subsurface property fields from one-dimensional measurement data (e.g., time series), the…

physics.geo-ph2018

Gradient-based deterministic inversion of geophysical data with Generative Adversarial Networks: is it feasible?

Eric Laloy, Niklas Linde, Cyprien Ruffino +3

Global probabilistic inversion within the latent space learned by a Generative Adversarial Network (GAN) has been recently demonstrated. Compared to inversion on the original model…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.