Fourier Basis Density Model

no code implementations • 23 Feb 2024 • Alfredo De la Fuente, Saurabh Singh, Johannes Ballé

We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis.

SaLinA: Sequential Learning of Agents

1 code implementation • 15 Oct 2021 • Ludovic Denoyer, Alfredo De la Fuente, Song Duong, Jean-Baptiste Gaya, Pierre-Alexandre Kamienny, Daniel H. Thompson

SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms.

reinforcement-learning Reinforcement Learning (RL)

Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks

no code implementations • 22 May 2018 • Lukas Mosser, Wouter Kimman, Jesper Dramsch, Steve Purves, Alfredo De la Fuente, Graham Ganssle

Traditional physics-based approaches to infer sub-surface properties such as full-waveform inversion or reflectivity inversion are time-consuming and computationally expensive.

Generative Adversarial Network